Human protein diversity arises as a result of alternative splicing, single nucleotide polymorphisms (SNPs) and posttranslational modifications. Because of these processes, each protein can exists as multiple variants in vivo. Tailored strategies are needed to study these protein variants and understand their role in health and disease. In this work we utilized quantitative mass spectrometric immunoassays to determine the protein variants concentration of beta-2-microglobulin, cystatin C, retinol binding protein, and transthyretin, in a population of 500 healthy individuals. Additionally, we determined the longitudinal concentration changes for the protein variants from four individuals over a 6 month period. Along with the native forms of the four proteins, 13 posttranslationally modified variants and 7 SNP-derived variants were detected and their concentration determined. Correlations of the variants concentration with geographical origin, gender, and age of the individuals were also examined. This work represents an important step toward building a catalog of protein variants concentrations and examining their longitudinal changes.

Background: Meiotic recombination has traditionally been explained based on the structural requirement to stabilize homologous chromosome pairs to ensure their proper meiotic segregation. Competing hypotheses seek to explain the emerging findings of significant heterogeneity in recombination rates within and between genomes, but intraspecific comparisons of genome-wide recombination patterns are rare. The honey bee (Apis mellifera) exhibits the highest rate of genomic recombination among multicellular animals with about five cross-over events per chromatid.

Results: Here, we present a comparative analysis of recombination rates across eight genetic linkage maps of the honey bee genome to investigate which genomic sequence features are correlated with recombination rate and with its variation across the eight data sets, ranging in average marker spacing ranging from 1 Mbp to 120 kbp. Overall, we found that GC content explained best the variation in local recombination rate along chromosomes at the analyzed 100 kbp scale. In contrast, variation among the different maps was correlated to the abundance of microsatellites and several specific tri- and tetra-nucleotides.

Conclusions: The combined evidence from eight medium-scale recombination maps of the honey bee genome suggests that recombination rate variation in this highly recombining genome might be due to the DNA configuration instead of distinct sequence motifs. However, more fine-scale analyses are needed. The empirical basis of eight differing genetic maps allowed for robust conclusions about the correlates of the local recombination rates and enabled the study of the relation between DNA features and variability in local recombination rates, which is particularly relevant in the honey bee genome with its exceptionally high recombination rate.

Proteins can exist as multiple proteoforms in vivo, as a result of alternative splicing and single-nucleotide polymorphisms (SNPs), as well as posttranslational processing. To address their clinical significance in a context of diagnostic information, proteoforms require a more in-depth analysis. Mass spectrometric immunoassays (MSIA) have been devised for studying structural diversity in human proteins. MSIA enables protein profiling in a simple and high-throughput manner, by combining the selectivity of targeted immunoassays, with the specificity of mass spectrometric detection. MSIA has been used for qualitative and quantitative analysis of single and multiple proteoforms, distinguishing between normal fluctuations and changes related to clinical conditions. This mini review offers an overview of the development and application of mass spectrometric immunoassays for clinical and population proteomics studies. Provided are examples of some recent developments, and also discussed are the trends and challenges in mass spectrometry-based immunoassays for the next-phase of clinical applications.

Honeybee workers are essentially sterile female helpers that make up the majority of individuals in a colony. Workers display a marked change in physiology when they transition from in-nest tasks to foraging. Recent technological advances have made it possible to unravel the metabolic modifications associated with this transition. Previous studies have revealed extensive remodeling of brain, thorax, and hypopharyngeal gland biochemistry. However, data on changes in the abdomen is scarce. To narrow this gap we investigated the proteomic composition of abdominal tissue in the days typically preceding the onset of foraging in honeybee workers.

In order to get a broader representation of possible protein dynamics, we used workers of two genotypes with differences in the age at which they initiate foraging. This approach was combined with RNA interference-mediated downregulation of an insulin/insulin-like signaling component that is central to foraging behavior, the insulin receptor substrate (irs), and with measurements of glucose and lipid levels.
Our data provide new insight into the molecular underpinnings of phenotypic plasticity in the honeybee, invoke parallels with vertebrate metabolism, and support an integrated and irs-dependent association of carbohydrate and lipid metabolism with the transition from in-nest tasks to foraging.

Introduction: Apolipoprotein C-III (apoC-III) regulates triglyceride (TG) metabolism. In plasma, apoC-III exists in non-sialylated (apoC-III_0a without glycosylation and apoC-III[subscript 0b] with glycosylation), monosialylated (apoC-III₁) or disialylated (apoC-III₂) proteoforms. Our aim was to clarify the relationship between apoC-III sialylation proteoforms with fasting plasma TG concentrations.

Methods: In 204 non-diabetic adolescent participants, the relative abundance of apoC-III plasma proteoforms was measured using mass spectrometric immunoassay.

Results: Compared with the healthy weight subgroup (n = 16), the ratios of apoC-III_0a, apoC-III_0b, and apoC-III₁ to apoC-III₂ were significantly greater in overweight (n = 33) and obese participants (n = 155). These ratios were positively correlated with BMI z-scores and negatively correlated with measures of insulin sensitivity (S[subscript i]). The relationship of apoC-III₁ / apoC-III₂ with S_i persisted after adjusting for BMI (p = 0.02). Fasting TG was correlated with the ratio of apoC-III_0a / apoC-III₂ (r = 0.47, p<0.001), apoC-III_0b / apoC-III₂ (r = 0.41, p<0.001), apoC-III₁ / apoC-III₂ (r = 0.43, p<0.001). By examining apoC-III concentrations, the association of apoC-III proteoforms with TG was driven by apoC-III_0a (r = 0.57, p<0.001), apoC-III_0b (r = 0.56. p<0.001) and apoC-III₁ (r = 0.67, p<0.001), but not apoC-III₂ (r = 0.006, p = 0.9) concentrations, indicating that apoC-III relationship with plasma TG differed in apoC-III₂ compared with the other proteoforms.

Conclusion: We conclude that apoC-III_0a, apoC-III_0b, and apoC-III₁, but not apoC-III₂ appear to be under metabolic control and associate with fasting plasma TG. Measurement of apoC-III proteoforms can offer insights into the biology of TG metabolism in obesity.

Background: Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. Despite new challenges, immunosignaturing gives us the opportunity to explore new and fundamentally different research questions. In addition to classifying samples based on disease status, the complex patterns and latent factors underlying immunosignatures, which we attempt to model, may have a diverse range of applications.

Methods: We investigate the utility of a number of statistical methods to determine model performance and address challenges inherent in analyzing immunosignatures. Some of these methods include exploratory and confirmatory factor analyses, classical significance testing, structural equation and mixture modeling.

Results: We demonstrate an ability to classify samples based on disease status and show that immunosignaturing is a very promising technology for screening and presymptomatic screening of disease. In addition, we are able to model complex patterns and latent factors underlying immunosignatures. These latent factors may serve as biomarkers for disease and may play a key role in a bioinformatic method for antibody discovery.

Conclusion: Based on this research, we lay out an analytic framework illustrating how immunosignatures may be useful as a general method for screening and presymptomatic screening of disease as well as antibody discovery.

Background: Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2¹⁶ - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.

Results: We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.

Conclusions: The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.

Background: High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.

Results: We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.

Conclusions: ‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.

The impetus for discovery and evaluation of protein biomarkers has been accelerated by recent development of advanced technologies for rapid and broad proteome analyses. Mass spectrometry (MS)-based protein assays hold great potential for in vitro biomarker studies. Described here is the development of a multiplex mass spectrometric immunoassay (MSIA) for quantification of apolipoprotein C-I (apoC-I), apolipoprotein C-II (apoC-II), apolipoprotein C-III (apoC-III) and their proteoforms. The multiplex MSIA assay was fast (∼40 min) and high-throughput (96 samples at a time). The assay was applied to a small cohort of human plasma samples, revealing the existence of multiple proteoforms for each apolipoprotein C. The quantitative aspect of the assay enabled determination of the concentration for each proteoform individually. Low-abundance proteoforms, such as fucosylated apoC-III, were detected in less than 20% of the samples. The distribution of apoC-III proteoforms varied among samples with similar total apoC-III concentrations. The multiplex analysis of the three apolipoproteins C and their proteoforms using quantitative MSIA represents a significant step forward toward better understanding of their physiological roles in health and disease.

Recent advancements in genomics provide new tools for evolutionary ecological research. The paper wasp genus Polistes is a model for social insect evolution and behavioral ecology. We developed RNA interference (RNAi)-mediated gene silencing to explore proposed connections between expression of hexameric storage proteins and worker vs. gyne (potential future foundress) castes in naturally-founded colonies of P. metricus. We extended four fragments of putative hexamerin-encoding P. metricus transcripts acquired from a previous study and fully sequenced a gene that encodes Hexamerin 2, one of two proposed hexameric storage proteins of P. metricus. MALDI-TOF/TOF, LC-MSMS, deglycosylation, and detection of phosphorylation assays showed that the two putative hexamerins diverge in peptide sequence and biochemistry. We targeted the hexamerin 2 gene in 5th (last)-instar larvae by feeding RNAi-inducing double-stranded hexamerin 2 RNA directly to larvae in naturally-founded colonies in the field. Larval development and adult traits were not significantly altered in hexamerin 2 knockdowns, but there were suggestive trends toward increased developmental time and less developed ovaries, which are gyne characteristics. By demonstrating how data acquisition from 454/Roche pyrosequencing can be combined with biochemical and proteomics assays and how RNAi can be deployed successfully in field experiments on Polistes, our results pave the way for functional genomic research that can contribute significantly to learning the interactions of environment, development, and the roles they play in paper wasp evolution and behavioral ecology.