The oxidative modification of apolipoprotein A-I’s methionine148 (M148) is associated with defective HDL function in vitro. Multiple reaction monitoring (MRM) is a mass spectrometric technique that can be used to quantitate post-translational modifications. In this study, we developed an MRM assay to monitor the abundance ratio of the peptide containing oxidized M148 to the native peptide in ApoA-I. Measurement of the oxidized-to-unoxidized-M148 ratio was reproducible (CV < 5%). The extent of methionine M148 oxidation in the HDL of healthy controls, and type 2 diabetic participants with and without prior cardiovascular events (CVD) were then examined. The results suggest a significant increase in the relative ratio of the peptide containing oxidized M148 to the unmodified peptide in the HDL of participants with diabetes and CVD (p < 0.001), compared to participants without CVD. Monitoring the abundance ratio of the peptides containing oxidized and unoxidized M148 by MRM provides a means of examining the relationship between M148 oxidation and vascular complications in CVD.

The most common evolutionary events at the molecular level are single-base substitutions, as well as insertions and deletions (indels) of short DNA segments. A large body of research has been devoted to develop probabilistic substitution models and to infer their parameters using likelihood and Bayesian approaches. In contrast, relatively little has been done to model indel dynamics, probably due to the difficulty in writing explicit likelihood functions. Here, we contribute to the effort of modeling indel dynamics by presenting SpartaABC, an approximate Bayesian computation (ABC) approach to infer indel parameters from sequence data (either aligned or unaligned). SpartaABC circumvents the need to use an explicit likelihood function by extracting summary statistics from simulated sequences. First, summary statistics are extracted from the input sequence data. Second, SpartaABC samples indel parameters from a prior distribution and uses them to simulate sequences. Third, it computes summary statistics from the simulated sets of sequences. By computing a distance between the summary statistics extracted from the input and each simulation, SpartaABC can provide an approximation to the posterior distribution of indel parameters as well as point estimates. We study the performance of our methodology and show that it provides accurate estimates of indel parameters in simulations. We next demonstrate the utility of SpartaABC by studying the impact of alignment errors on the inference of positive selection. A C ++ program implementing SpartaABC is freely available in http://spartaabc.tau.ac.il.

Under models of isolation-by-distance, population structure is determined by the probability of identity-by-descent between pairs of genes according to the geographic distance between them. Well established analytical results indicate that the relationship between geographical and genetic distance depends mostly on the neighborhood size of the population which represents a standardized measure of gene flow. To test this prediction, we model local dispersal of haploid individuals on a two-dimensional landscape using seven dispersal kernels: Rayleigh, exponential, half-normal, triangular, gamma, Lomax and Pareto. When neighborhood size is held constant, the distributions produce similar patterns of isolation-by-distance, confirming predictions. Considering this, we propose that the triangular distribution is the appropriate null distribution for isolation-by-distance studies. Under the triangular distribution, dispersal is uniform over the neighborhood area which suggests that the common description of neighborhood size as a measure of an effective, local panmictic population is valid for popular families of dispersal distributions. We further show how to draw random variables from the triangular distribution efficiently and argue that it should be utilized in other studies in which computational efficiency is important.

Mutation is the ultimate source of all genetic variation and is, therefore, central to evolutionary change. Previous work on Paramecium tetraurelia found an unusually low germline base-substitution mutation rate in this ciliate. Here, we tested the generality of this result among ciliates using Tetrahymena thermophila. We sequenced the genomes of 10 lines of T. thermophila that had each undergone approximately 1,000 generations of mutation accumulation (MA). We applied an existing mutation-calling pipeline and developed a new probabilistic mutation detection approach that directly models the design of an MA experiment and accommodates the noise introduced by mismapped reads. Our probabilistic mutation-calling method provides a straightforward way of estimating the number of sites at which a mutation could have been called if one was present, providing the denominator for our mutation rate calculations. From these methods, we find that T. thermophila has a germline base-substitution mutation rate of 7.61 × 10 ^-12 per-site, per cell division, which is consistent with the low base-substitution mutation rate in P. tetraurelia. Over the course of the evolution experiment, genomic exclusion lines derived from the MA lines experienced a fitness decline that cannot be accounted for by germline base-substitution mutations alone, suggesting that other genetic or epigenetic factors must be involved. Because selection can only operate to reduce mutation rates based upon the "visible" mutational load, asexual reproduction with a transcriptionally silent germline may allow ciliates to evolve extremely low germline mutation rates.

Inbreeding in hermaphroditic plants can occur through two different mechanisms: biparental inbreeding, when a plant mates with a related individual, or self-fertilization, when a plant mates with itself. To avoid inbreeding, many hermaphroditic plants have evolved self-incompatibility (SI) systems which prevent or limit self-fertilization. One particular SI system—homomorphic SI—can also reduce biparental inbreeding. Homomorphic SI is found in many angiosperm species, and it is often assumed that the additional benefit of reduced biparental inbreeding may be a factor in the success of this SI system. To test this assumption, we developed a spatially-explicit, individual-based simulation of plant populations that displayed three different types of homomorphic SI. We measured the total level of inbreeding avoidance by comparing each population to a self-compatible population (NSI), and we measured biparental inbreeding avoidance by comparing to a population of self-incompatible plants that were free to mate with any other individual (PSI).

Because biparental inbreeding is more common when offspring dispersal is limited, we examined the levels of biparental inbreeding over a range of dispersal distances. We also tested whether the introduction of inbreeding depression affected the level of biparental inbreeding avoidance. We found that there was a statistically significant decrease in autozygosity in each of the homomorphic SI populations compared to the PSI population and, as expected, this was more pronounced when seed and pollen dispersal was limited. However, levels of homozygosity and inbreeding depression were not reduced. At low dispersal, homomorphic SI populations also suffered reduced female fecundity and had smaller census population sizes. Overall, our simulations showed that the homomorphic SI systems had little impact on the amount of biparental inbreeding in the population especially when compared to the overall reduction in inbreeding compared to the NSI population. With further study, this observation may have important consequences for research into the origin and evolution of homomorphic self-incompatibility systems.

Plasmodium vivax is the most prevalent malarial species in South America and exerts a substantial burden on the populations it affects. The control and eventual elimination of P. vivax are global health priorities. Genomic research contributes to this objective by improving our understanding of the biology of P. vivax and through the development of new genetic markers that can be used to monitor efforts to reduce malaria transmission. Here we analyze whole-genome data from eight field samples from a region in Cordóba, Colombia where malaria is endemic. We find considerable genetic diversity within this population, a result that contrasts with earlier studies suggesting that P. vivax had limited diversity in the Americas. We also identify a selective sweep around a substitution known to confer resistance to sulphadoxine-pyrimethamine (SP). This is the first observation of a selective sweep for SP resistance in this species. These results indicate that P. vivax has been exposed to SP pressure even when the drug is not in use as a first line treatment for patients afflicted by this parasite. We identify multiple non-synonymous substitutions in three other genes known to be involved with drug resistance in Plasmodium species. Finally, we found extensive microsatellite polymorphisms. Using this information we developed 18 polymorphic and easy to score microsatellite loci that can be used in epidemiological investigations in South America.

In this study, we present a novel methodology to infer indel parameters from multiple sequence alignments (MSAs) based on simulations. Our algorithm searches for the set of evolutionary parameters describing indel dynamics which best fits a given input MSA. In each step of the search, we use parametric bootstraps and the Mahalanobis distance to estimate how well a proposed set of parameters fits input data. Using simulations, we demonstrate that our methodology can accurately infer the indel parameters for a large variety of plausible settings. Moreover, using our methodology, we show that indel parameters substantially vary between three genomic data sets: Mammals, bacteria, and retroviruses. Finally, we demonstrate how our methodology can be used to simulate MSAs based on indel parameters inferred from real data sets.

Mathematical models of infectious diseases are a valuable tool in understanding the mechanisms and patterns of disease transmission. It is, however, a difficult subject to teach, requiring both mathematical expertise and extensive subject-matter knowledge of a variety of disease systems. In this article, we explore several uses of zombie epidemics to make mathematical modeling and infectious disease epidemiology more accessible to public health professionals, students, and the general public. We further introduce a web-based simulation, White Zed (http://cartwrig.ht/apps/whitezed/), that can be deployed in classrooms to allow students to explore models before implementing them. In our experience, zombie epidemics are familiar, approachable, flexible, and an ideal way to introduce basic concepts of infectious disease epidemiology.

Background: The cytokine MIF (Macrophage Migration Inhibitory Factor) has diverse physiological roles and is present at elevated concentrations in numerous disease states. However, its molecular heterogeneity has not been previously investigated in biological samples. Mass Spectrometric Immunoassay (MSIA) may help elucidate MIF post-translational modifications existing in vivo and provide additional clarity regarding its relationship to diverse pathologies.

Results: In this work, we have developed and validated a fully quantitative MSIA assay for MIF, and used it in the discovery and quantification of different proteoforms of MIF in serum samples, including cysteinylated and glycated MIF. The MSIA assay had a linear range of 1.56-50 ng/mL, and exhibited good precision, linearity, and recovery characteristics. The new assay was applied to a small cohort of human serum samples, and benchmarked against an MIF ELISA assay.

Conclusions: The quantitative MIF MSIA assay provides a sensitive, precise and high throughput method to delineate and quantify MIF proteoforms in biological samples.

Background: Cystatin C (CysC) is an endogenous cysteine protease inhibitor that can be used to assess the progression of kidney function. Recent studies demonstrate that CysC is a more specific indicator of glomerular filtration rate (GFR) than creatinine. CysC in plasma exists in multiple proteoforms. The goal of this study was to clarify the association of native CysC, CysC missing N-terminal Serine (CysC des-S), and CysC without three N-terminal residues (CysC des-SSP) with diabetic chronic kidney disease (CKD).

Results: Using mass spectrometric immunoassay, the plasma concentrations of native CysC and the two CysC truncation proteoforms were examined in 111 individuals from three groups: 33 non-diabetic controls, 34 participants with type 2 diabetes (DM) and without CKD and 44 participants with diabetic CKD. Native CysC concentrations were 1.4 fold greater in CKD compared to DM group (p = 0.02) and 1.5 fold greater in CKD compared to the control group (p = 0.001). CysC des-S concentrations were 1.55 fold greater in CKD compared to the DM group (p = 0.002) and 1.9 fold greater in CKD compared to the control group (p = 0.0002). CysC des-SSP concentrations were 1.8 fold greater in CKD compared to the DM group (p = 0.008) and 1.52 fold greater in CKD compared to the control group (p = 0.002). In addition, the concentrations of CysC proteoforms were greater in the setting of albuminuria. The truncated CysC proteoform concentrations were associated with estimated GFR independent of native CysC concentrations.

Conclusion: Our findings demonstrate a greater amount of CysC proteoforms in diabetic CKD. We therefore suggest assessing the role of cystatin C proteoforms in the progression of CKD.