Proportionality: A Valid Alternative to Correlation for Relative Data

In the life sciences, many measurement methods yield only the relative abundances of different components in a sample. With such relative—or compositional—data, differential expression needs careful interpretation, and correlation—a statistical workhorse for analyzing pairwise relationships—is an inappropriate measure of association. Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data. We show how the strength of proportionality between two variables can be meaningfully and interpretably described by a new statistic ϕ which can be used instead of correlation as the basis of familiar analyses and visualisation methods, including co-expression networks and clustered heatmaps. While the main aim of this study is to present proportionality as a means to analyse relative data, it also raises intriguing questions about the molecular mechanisms underlying the proportional regulation of a range of yeast genes.     

Predicting distributions of Wolbachia strains through host ecological contact—Who's manipulating whom?

Reproductive isolation in response to divergent selection is often mediated via third‐party interactions. Under these conditions, speciation is inextricably linked to ecological context. We present a novel framework for understanding arthropod speciation as mediated by Wolbachia, a microbial endosymbiont capable of causing host cytoplasmic incompatibility (CI). We predict that sympatric host sister‐species harbor paraphyletic Wolbachia strains that provide CI, while well‐defined congeners in ecological contact and recently diverged noninteracting congeners are uninfected due to Wolbachia redundancy. We argue that Wolbachia provides an adaptive advantage when coupled with reduced hybrid fitness, facilitating assortative mating between co‐occurring divergent phenotypes—the contact contingency hypothesis. To test this, we applied a predictive algorithm to empirical pollinating fig wasp data, achieving up to 91.60% accuracy. We further postulate that observed temporal decay of Wolbachia incidence results from adaptive host purging—adaptive decay hypothesis—but implementation failed to predict systematic patterns. We then account for post‐zygotic offspring mortality during CI mating, modeling fitness clines across developmental resources—the fecundity trade‐off hypothesis. This model regularly favored CI despite fecundity losses. We demonstrate that a rules‐based algorithm accurately predicts Wolbachia infection status. This has implications among other systems where closely related sympatric species encounter adaptive disadvantage through hybridization.     

Modeling mycorrhizal fungi dispersal by the mycophagous swamp wallaby (Wallabia bicolor)

Despite the importance of mammal‐fungal interactions, tools to estimate the mammal‐assisted dispersal distances of fungi are lacking. Many mammals actively consume fungal fruiting bodies, the spores of which remain viable after passage through their digestive tract. Many of these fungi form symbiotic relationships with trees and provide an array of other key ecosystem functions. We present a flexible, general model to predict the distance a mycophagous mammal would disperse fungal spores. We modeled the probability of spore dispersal by combining animal movement data from GPS telemetry with data on spore gut‐retention time. We test this model using an exemplar generalist mycophagist, the swamp wallaby (Wallabia bicolor). We show that swamp wallabies disperse fungal spores hundreds of meters—and occasionally up to 1,265 m—from the point of consumption, distances that are ecologically significant for many mycorrhizal fungi. In addition to highlighting the ecological importance of swamp wallabies as dispersers of mycorrhizal fungi in eastern Australia, our simple modeling approach provides a novel and effective way of empirically describing spore dispersal by a mycophagous animal. This approach is applicable to the study of other animal‐fungi interactions in other ecosystems.     

Learning a Prior on Regulatory Potential from eQTL Data

Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of possibilities. The problem is particularly challenging in populations with significant linkage disequilibrium, where traits are often linked to large chromosomal regions containing many genes. Here, we present a novel method, Lirnet, that automatically learns a regulatory potential for each sequence polymorphism, estimating how likely it is to have a significant effect on gene expression. This regulatory potential is defined in terms of “regulatory features”—including the function of the gene and the conservation, type, and position of genetic polymorphisms—that are available for any organism. The extent to which the different features influence the regulatory potential is learned automatically, making Lirnet readily applicable to different datasets, organisms, and feature sets. We apply Lirnet both to the human HapMap eQTL dataset and to a yeast eQTL dataset and provide statistical and biological results demonstrating that Lirnet produces significantly better regulatory programs than other recent approaches. We demonstrate in the yeast data that Lirnet can correctly suggest a specific causal sequence variation within a large, linked chromosomal region. In one example, Lirnet uncovered a novel, experimentally validated connection between Puf3—a sequence-specific RNA binding protein—and P-bodies—cytoplasmic structures that regulate translation and RNA stability—as well as the particular causative polymorphism, a SNP in Mkt1, that induces the variation in the pathway.     

Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics

Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations—such as measurement error—as compared to systematic deviations—such as individual differences—are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual’s samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error.     

An inclusive fitness analysis of synergistic interactions in structured populations

We study the evolution of a pair of competing behavioural alleles in a structured population when there are non-additive or ‘synergistic’ fitness effects. Under a form of weak selection and with a simple symmetry condition between a pair of competing alleles, Tarnita et al. provide a surprisingly simple condition for one allele to dominate the other. Their condition can be obtained from an analysis of a corresponding simpler model in which fitness effects are additive. Their result uses an average measure of selective advantage where the average is taken over the long-term—that is, over all possible allele frequencies—and this precludes consideration of any frequency dependence the allelic fitness might exhibit. However, in a considerable body of work with non-additive fitness effects—for example, hawk–dove and prisoner''s dilemma games—frequency dependence plays an essential role in the establishment of conditions for a stable allele-frequency equilibrium. Here, we present a frequency-dependent generalization of their result that provides an expression for allelic fitness at any given allele frequency p. We use an inclusive fitness approach and provide two examples for an infinite structured population. We illustrate our results with an analysis of the hawk–dove game.     

Mechanistic model of nutrient uptake explains dichotomy between marine oligotrophic and copiotrophic bacteria

Marine bacterial diversity is immense and believed to be driven in part by trade-offs in metabolic strategies. Here we consider heterotrophs that rely on organic carbon as an energy source and present a molecular-level model of cell metabolism that explains the dichotomy between copiotrophs—which dominate in carbon-rich environments—and oligotrophs—which dominate in carbon-poor environments—as the consequence of trade-offs between nutrient transport systems. While prototypical copiotrophs, like Vibrios, possess numerous phosphotransferase systems (PTS), prototypical oligotrophs, such as SAR11, lack PTS and rely on ATP-binding cassette (ABC) transporters, which use binding proteins. We develop models of both transport systems and use them in proteome allocation problems to predict the optimal nutrient uptake and metabolic strategy as a function of carbon availability. We derive a Michaelis–Menten approximation of ABC transport, analytically demonstrating how the half-saturation concentration is a function of binding protein abundance. We predict that oligotrophs can attain nanomolar half-saturation concentrations using binding proteins with only micromolar dissociation constants and while closely matching transport and metabolic capacities. However, our model predicts that this requires large periplasms and that the slow diffusion of the binding proteins limits uptake. Thus, binding proteins are critical for oligotrophic survival yet severely constrain growth rates. We propose that this trade-off fundamentally shaped the divergent evolution of oligotrophs and copiotrophs.     

SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor

Identifying discriminative motifs underlying the functionality and evolution of organisms is a major challenge in computational biology. Machine learning approaches such as support vector machines (SVMs) achieve state-of-the-art performances in genomic discrimination tasks, but—due to its black-box character—motifs underlying its decision function are largely unknown. As a remedy, positional oligomer importance matrices (POIMs) allow us to visualize the significance of position-specific subsequences. Although being a major step towards the explanation of trained SVM models, they suffer from the fact that their size grows exponentially in the length of the motif, which renders their manual inspection feasible only for comparably small motif sizes, typically k ≤ 5. In this work, we extend the work on positional oligomer importance matrices, by presenting a new machine-learning methodology, entitled motifPOIM, to extract the truly relevant motifs—regardless of their length and complexity—underlying the predictions of a trained SVM model. Our framework thereby considers the motifs as free parameters in a probabilistic model, a task which can be phrased as a non-convex optimization problem. The exponential dependence of the POIM size on the oligomer length poses a major numerical challenge, which we address by an efficient optimization framework that allows us to find possibly overlapping motifs consisting of up to hundreds of nucleotides. We demonstrate the efficacy of our approach on a synthetic data set as well as a real-world human splice site data set.     

Complex Genomic Rearrangements at the PLP1 Locus Include Triplication and Quadruplication

Inverted repeats (IRs) can facilitate structural variation as crucibles of genomic rearrangement. Complex duplication—inverted triplication—duplication (DUP-TRP/INV-DUP) rearrangements that contain breakpoint junctions within IRs have been recently associated with both MECP2 duplication syndrome (MIM#300260) and Pelizaeus-Merzbacher disease (PMD, MIM#312080). We investigated 17 unrelated PMD subjects with copy number gains at the PLP1 locus including triplication and quadruplication of specific genomic intervals—16/17 were found to have a DUP-TRP/INV-DUP rearrangement product. An IR distal to PLP1 facilitates DUP-TRP/INV-DUP formation as well as an inversion structural variation found frequently amongst normal individuals. We show that a homology—or homeology—driven replicative mechanism of DNA repair can apparently mediate template switches within stretches of microhomology. Moreover, we provide evidence that quadruplication and potentially higher order amplification of a genomic interval can occur in a manner consistent with rolling circle amplification as predicted by the microhomology-mediated break induced replication (MMBIR) model.     

High Abundances of Potentially Active Ammonia-Oxidizing Bacteria and Archaea in Oligotrophic,High-Altitude Lakes of the Sierra Nevada,California, USA

Nitrification plays a central role in the nitrogen cycle by determining the oxidation state of nitrogen and its subsequent bioavailability and cycling. However, relatively little is known about the underlying ecology of the microbial communities that carry out nitrification in freshwater ecosystems—and particularly within high-altitude oligotrophic lakes, where nitrogen is frequently a limiting nutrient. We quantified ammonia-oxidizing archaea (AOA) and bacteria (AOB) in 9 high-altitude lakes (2289–3160 m) in the Sierra Nevada, California, USA, in relation to spatial and biogeochemical data. Based on their ammonia monooxygenase (amoA) genes, AOB and AOA were frequently detected. AOB were present in 88% of samples and were more abundant than AOA in all samples. Both groups showed >100 fold variation in abundance between different lakes, and were also variable through time within individual lakes. Nutrient concentrations (ammonium, nitrite, nitrate, and phosphate) were generally low but also varied across and within lakes, suggestive of active internal nutrient cycling; AOB abundance was significantly correlated with phosphate (r² = 0.32, p<0.1), whereas AOA abundance was inversely correlated with lake elevation (r² = 0.43, p<0.05). We also measured low rates of ammonia oxidation—indicating that AOB, AOA, or both, may be biogeochemically active in these oligotrophic ecosystems. Our data indicate that dynamic populations of AOB and AOA are found in oligotrophic, high-altitude, freshwater lakes.     

Food or just a free ride? A meta-analysis reveals the global diversity of the Plastisphere

It is now indisputable that plastics are ubiquitous and problematic in ecosystems globally. Many suggestions have been made about the role that biofilms colonizing plastics in the environment—termed the “Plastisphere”—may play in the transportation and ecological impact of these plastics. By collecting and re-analyzing all raw 16S rRNA gene sequencing and metadata from 2,229 samples within 35 studies, we have performed the first meta-analysis of the Plastisphere in marine, freshwater, other aquatic (e.g., brackish or aquaculture) and terrestrial environments. We show that random forest models can be trained to differentiate between groupings of environmental factors as well as aspects of study design, but—crucially—also between plastics when compared with control biofilms and between different plastic types and community successional stages. Our meta-analysis confirms that potentially biodegrading Plastisphere members, the hydrocarbonoclastic Oceanospirillales and Alteromonadales are consistently more abundant in plastic than control biofilm samples across multiple studies and environments. This indicates the predilection of these organisms for plastics and confirms the urgent need for their ability to biodegrade plastics to be comprehensively tested. We also identified key knowledge gaps that should be addressed by future studies.Subject terms: Microbial ecology, Soil microbiology, Water microbiology, Microbial ecology, Microbiome     

Rate of Gene Silencing at Duplicate Loci: A Theoretical Study and Interpretation of Data from Tetraploid Fishes

A large-scale simulation has been conducted on the rate of gene loss at duplicate loci under irreversible mutation. It is found that tight linkage does not provide a strong sheltering effect, as thought by previous authors; indeed, the mean loss time for the case of tight linkage is of the same order of magnitude as that for no linkage, as long as Nu is not much larger than 1, where N is the effective population size and u the mutation rate. When Nu is 0.01 or less, the two loci behave almost as neutral loci, regardless of linkage, and the mean loss time is about only half the mean extinction time for a neutral allele under irreversible mutation. However, the former becomes two or more times larger than the latter when Nu ≥ 1.——In the simulation, the sojourn times in the frequency intervals (0, 0.01) and (0.99, 1) and the time for the frequency of the null allele to reach 0.99 at one of the two loci have also been recorded. The results show that the population is monomorphic for the normal allele most of the time if Nu ≤ 0.01, but polymorphic for the null and the normal alleles most of the time if Nu ≥ 0.1.——The distribution of the frequency of the null allele in an equilibrium tetraploid population has been studied analytically. The present results have been applied to interpret data from some fish groups that are of tetraploid origin, and a model for explaining the slow rate of gene loss in these fishes is proposed.     

Stress-induced mutagenesis and complex adaptation

Because mutations are mostly deleterious, mutation rates should be reduced by natural selection. However, mutations also provide the raw material for adaptation. Therefore, evolutionary theory suggests that the mutation rate must balance between adaptability—the ability to adapt—and adaptedness—the ability to remain adapted. We model an asexual population crossing a fitness valley and analyse the rate of complex adaptation with and without stress-induced mutagenesis (SIM)—the increase of mutation rates in response to stress or maladaptation. We show that SIM increases the rate of complex adaptation without reducing the population mean fitness, thus breaking the evolutionary trade-off between adaptability and adaptedness. Our theoretical results support the hypothesis that SIM promotes adaptation and provide quantitative predictions of the rate of complex adaptation with different mutational strategies.     

Comparison of Four Agar Plating Media with and Without Added Novobiocin for Isolation of Salmonellae from Beef and Deboned Poultry Meat

Four plating media, Hektoen enteric (HE), xylose-lysine deoxycholate (XLD), tryptic soy-xylose-lysine (TSXL), and tryptic soy-brillant green (TSBG) agars with and without 10 mg of added novobiocin per ml, were evaluated for recovery of Salmonella from roast beef and deboned turkey. Colonies producing a reaction typical of H₂S-positive salmonellae (alkaline with black centers) were picked. On the media without novobiocin, from 109 determinations on 75 samples, number of salmonellae found and false-positives were, respectively: HE—13, 58; XLD—17, 18; TSXL—23, 0; TSBG—22, 7. When novobiocin was present the corresponding results were: HE—17, 24; XLD—21, 2; TSXL—23, 3; TSBG—20, 7. A total of 25 determinations were positive on one or more agars. False-positives on HE and XLD without novobiocin were predominantly Proteus, which were almost totally eliminated by addition of 10 mg of novobiocin per liter. If alkaline H₂S-negative colonies had been considered, many more false-positives would have been found on HE and XLD but not on TSBG or TSXL. Addition of novobiocin markedly improved isolations of salmonellae from XLD and HE and reduced the number of false-positives. Addition of novobiocin did not improve performance of TSXL and slightly impaired differentiation of salmonellae from Citrobacter on TSBG. XLD with novobiocin and TSXL are highly specific for H₂S-positive salmonellae, and the appearance of Salmonella-like colonies on these media can be considered a presumptive test for H₂S-positive salmonellae.     

DNA barcodes reveal species-specific mercury levels in tuna sushi that pose a health risk to consumers

Excessive ingestion of mercury—a health hazard associated with consuming predatory fishes—damages neurological, sensory-motor and cardiovascular functioning. The mercury levels found in Bigeye Tuna (Thunnus obesus) and bluefin tuna species (Thunnus maccoyii, Thunnus orientalis, and Thunnus thynnus), exceed or approach levels permissible by Canada, the European Union, Japan, the US, and the World Health Organization. We used DNA barcodes to identify tuna sushi samples analysed for mercury and demonstrate that the ability to identify cryptic samples in the market place allows regulatory agencies to more accurately measure the risk faced by fish consumers and enact policies that better safeguard their health.     

Intelligent high‐throughput intervention testing platform in Daphnia

We present a novel platform for testing the effects of interventions on the life‐ and healthspan of a short‐lived freshwater organism with complex behavior and physiology—the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioral features of both routine motion and response to stimuli are continuously quantified over large synchronized cohorts via an automated phenotyping pipeline. We build predictive machine‐learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life‐long and instant assays as illustrated using a long‐term dose‐response profile of metformin and a short‐term assay of well‐studied substances such as caffeine and alcohol.     

Electrophoretic Variation for X Chromosome-Linked Hypoxanthine Phosphoribosyl Transferase (Hprt) in Wild-Derived Mice

An electrophoretic variation for hypoxanthine phosphoribosyltransferase, HPRT, has been identified in samples of Mus spretus, a field mouse from southern Europe and in M. m. castaneus, a house mouse from southeast Asia. These mice will interbreed with laboratory mice to produce viable, fertile F₁ progeny. The variation for HPRT segregates as an X chromosome gene in F₁ and backcross progeny. Linkage analysis involving the markers Pgk-1 and Ags indicated a gene order of centromere— Hprt—Pgk-1—Ags in crosses involving both stocks of wild mice.     

Integrating GWAS and Expression Data for Functional Characterization of Disease-Associated SNPs: An Application to Follicular Lymphoma

Development of post-GWAS (genome-wide association study) methods are greatly needed for characterizing the function of trait-associated SNPs. Strategies integrating various biological data sets with GWAS results will provide insights into the mechanistic role of associated SNPs. Here, we present a method that integrates RNA sequencing (RNA-seq) and allele-specific expression data with GWAS data to further characterize SNPs associated with follicular lymphoma (FL). We investigated the influence on gene expression of three established FL-associated loci—rs10484561, rs2647012, and rs6457327—by measuring their correlation with human-leukocyte-antigen (HLA) expression levels obtained from publicly available RNA-seq expression data sets from lymphoblastoid cell lines. Our results suggest that SNPs linked to the protective variant rs2647012 exert their effect by a cis-regulatory mechanism involving modulation of HLA-DQB1 expression. In contrast, no effect on HLA expression was observed for the colocalized risk variant rs10484561. The application of integrative methods, such as those presented here, to other post-GWAS investigations will help identify causal disease variants and enhance our understanding of biological disease mechanisms.