Host plant‐related genomic differentiation in the European cherry fruit fly,Rhagoletis cerasi

Elucidating the mechanisms and conditions facilitating the formation of biodiversity are central topics in evolutionary biology. A growing number of studies imply that divergent ecological selection may often play a critical role in speciation by counteracting the homogenising effects of gene flow. Several examples involve phytophagous insects, where divergent selection pressures associated with host plant shifts may generate reproductive isolation, promoting speciation. Here, we use ddRADseq to assess the population structure and to test for host‐related genomic differentiation in the European cherry fruit fly, Rhagoletis cerasi (L., 1758) (Diptera: Tephritidae). This tephritid is distributed throughout Europe and western Asia, and has adapted to two different genera of host plants, Prunus spp. (cherries) and Lonicera spp. (honeysuckle). Our data imply that geographic distance and geomorphic barriers serve as the primary factors shaping genetic population structure across the species range. Locally, however, flies genetically cluster according to host plant, with consistent allele frequency differences displayed by a subset of loci between Prunus and Lonicera flies across four sites surveyed in Germany and Norway. These 17 loci display significantly higher F_ST values between host plants than others. They also showed high levels of linkage disequilibrium within and between Prunus and Lonicera flies, supporting host‐related selection and reduced gene flow. Our findings support the existence of sympatric host races in R. cerasi embedded within broader patterns of geographic variation in the fly, similar to the related apple maggot, Rhagoletis pomonella, in North America.     

A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets

Calculating the number of confidently identified proteins and estimating false discovery rate (FDR) is a challenge when analyzing very large proteomic data sets such as entire human proteomes. Biological and technical heterogeneity in proteomic experiments further add to the challenge and there are strong differences in opinion regarding the conceptual validity of a protein FDR and no consensus regarding the methodology for protein FDR determination. There are also limitations inherent to the widely used classic target–decoy strategy that particularly show when analyzing very large data sets and that lead to a strong over-representation of decoy identifications. In this study, we investigated the merits of the classic, as well as a novel target–decoy-based protein FDR estimation approach, taking advantage of a heterogeneous data collection comprised of ∼19,000 LC-MS/MS runs deposited in ProteomicsDB (https://www.proteomicsdb.org). The “picked” protein FDR approach treats target and decoy sequences of the same protein as a pair rather than as individual entities and chooses either the target or the decoy sequence depending on which receives the highest score. We investigated the performance of this approach in combination with q-value based peptide scoring to normalize sample-, instrument-, and search engine-specific differences. The “picked” target–decoy strategy performed best when protein scoring was based on the best peptide q-value for each protein yielding a stable number of true positive protein identifications over a wide range of q-value thresholds. We show that this simple and unbiased strategy eliminates a conceptual issue in the commonly used “classic” protein FDR approach that causes overprediction of false-positive protein identification in large data sets. The approach scales from small to very large data sets without losing performance, consistently increases the number of true-positive protein identifications and is readily implemented in proteomics analysis software.Shotgun proteomics is the most popular approach for large-scale identification and quantification of proteins. The rapid evolution of high-end mass spectrometers in recent years (1–5) has made proteomic studies feasible that identify and quantify as many as 10,000 proteins in a sample (6–8) and enables many lines of new scientific research including, for example, the analysis of many human proteomes, and proteome-wide protein–drug interaction studies (9–11). One fundamental step in most proteomic experiments is the identification of proteins in the biological system under investigation. To achieve this, proteins are digested into peptides, analyzed by LC-MS/MS, and tandem mass spectra are used to interrogate protein sequence databases using search engines that match experimental data to data generated in silico (12, 13). Peptide spectrum matches (PSMs)¹ are commonly assigned by a search engine using either a heuristic or a probabilistic scoring scheme (14–18). Proteins are then inferred from identified peptides and a protein score or a probability derived as a measure for the confidence in the identification (13, 19).Estimating the proportion of false matches (false discovery rate; FDR) in an experiment is important to assess and maintain the quality of protein identifications. Owing to its conceptual and practical simplicity, the most widely used strategy to estimate FDR in proteomics is the target–decoy database search strategy (target–decoy strategy; TDS) (20). The main assumption underlying this idea is that random matches (false positives) should occur with similar likelihood in the target database and the decoy (reversed, shuffled, or otherwise randomized) version of the same database (21, 22). The number of matches to the decoy database, therefore, provides an estimate of the number of random matches one should expect to obtain in the target database. The number of target and decoy hits can then be used to calculate either a local or a global FDR for a given data set (21–26). This general idea can be applied to control the FDR at the level of PSMs, peptides, and proteins, typically by counting the number of target and decoy observations above a specified score.Despite the significant practical impact of the TDS, it has been observed that a peptide FDR that results in an acceptable protein FDR (of say 1%) for a small or medium sized data set, turns into an unacceptably high protein FDR when the data set grows larger (22, 27). This is because the basic assumption of the classical TDS is compromised when a large proportion of the true positive proteins have already been identified. In small data sets, containing say only a few hundred to a few thousand proteins, random peptide matches will be distributed roughly equally over all decoy and “leftover” target proteins, allowing for a reasonably accurate estimation of false positive target identifications by using the number of decoy identifications. However, in large experiments comprising hundreds to thousands of LC-MS/MS runs, 10,000 or more target proteins may be genuinely and repeatedly identified, leaving an ever smaller number of (target) proteins to be hit by new false positive peptide matches. In contrast, decoy proteins are only hit by the occasional random peptide match but fully count toward the number of false positive protein identifications estimated from the decoy hits. The higher the number of genuinely identified target proteins gets, the larger this imbalance becomes. If this is not corrected for in the decoy space, an overestimation of false positives will occur.This problem has been recognized and e.g. Reiter and colleagues suggested a way for correcting for the overestimation of false positive protein hits termed MAYU (27). Following the main assumption that protein identifications containing false positive PSMs are uniformly distributed over the target database, MAYU models the number of false positive protein identifications using a hypergeometric distribution. Its parameters are estimated from the number of protein database entries and the total number of target and decoy protein identifications. The protein FDR is then estimated by dividing the number of expected false positive identifications (expectation value of the hypergeometric distribution) by the total number of target identifications. Although this approach was specifically designed for large data sets (tested on ∼1300 LC-MS/MS runs from digests of C. elegans proteins), it is not clear how far the approach actually scales. Another correction strategy for overestimation of false positive rates, the R factor, was suggested initially for peptides (28) and more recently for proteins (29). A ratio, R, of forward and decoy hits in the low probability range is calculated, where the number of true peptide or protein identifications is expected to be close to zero, and hence, R should approximate one. The number of decoy hits is then multiplied (corrected) by the R factor when performing FDR calculations. The approach is conceptually simpler than the MAYU strategy and easy to implement, but is also based on the assumption that the inflation of the decoy hits intrinsic in the classic target–decoy strategy occurs to the same extent in all probability ranges.In the context of the above, it is interesting to note that there is currently no consensus in the community regarding if and how protein FDRs should be calculated for data of any size. One perhaps extreme view is that, owing to issues and assumptions related to the peptide to protein inference step and ways of constructing decoy protein sequences, protein level FDRs cannot be meaningfully estimated at all (30). This is somewhat unsatisfactory as an estimate of protein level error in proteomic experiments is highly desirable. Others have argued that target–decoy searches are not even needed when accurate p values of individual PSMs are available (31) whereas others choose to tighten the PSM or peptide FDRs obtained from TDS analysis to whatever threshold necessary to obtain a desired protein FDR (32). This is likely too conservative.We have recently proposed an alternative protein FDR approach termed “picked” target–decoy strategy (picked TDS) that indicated improved performance over the classical TDS in a very large proteomic data set (9) but a systematic investigation of the idea had not been performed at the time. In this study, we further characterized the picked TDS for protein FDR estimation and investigated its scalability compared with that of the classic TDS FDR method in data sets of increasing size up to ∼19,000 LC-MS/MS runs. The results show that the picked TDS is effective in preventing decoy protein over-representation, identifies more true positive hits, and works equally well for small and large proteomic data sets.     

Reproducible quantitative proteotype data matrices for systems biology

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.     

Altitudinal patterns of diversity and functional traits of metabolically active microorganisms in stream biofilms

Resources structure ecological communities and potentially link biodiversity to energy flow. It is commonly believed that functional traits (generalists versus specialists) involved in the exploitation of resources depend on resource availability and environmental fluctuations. The longitudinal nature of stream ecosystems provides changing resources to stream biota with yet unknown effects on microbial functional traits and community structure. We investigated the impact of autochthonous (algal extract) and allochthonous (spruce extract) resources, as they change along alpine streams from above to below the treeline, on microbial diversity, community composition and functions of benthic biofilms. Combining bromodeoxyuridine labelling and 454 pyrosequencing, we showed that diversity was lower upstream than downstream of the treeline and that community composition changed along the altitudinal gradient. We also found that, especially for allochthonous resources, specialisation by biofilm bacteria increased along that same gradient. Our results suggest that in streams below the treeline biofilm diversity, specialisation and functioning are associated with increasing niche differentiation as potentially modulated by divers allochthonous and autochthonous constituents contributing to resources. These findings expand our current understanding on biofilm structure and function in alpine streams.     

Different conformational forms of serum carnosinase detected by a newly developed sandwich ELISA for the measurements of carnosinase concentrations

Serum carnosinase (CN-1) measurements are at present mainly performed by assessing enzyme activity. This method is time-consuming, not well suited for large series of samples and can be discordant to measurements of CN-1 protein concentrations. To overcome these limitations, we developed sandwich ELISA assays using different anti-CN-1 antibodies, i.e., ATLAS (polyclonal IgG) and RYSK173 (monoclonal IgG1). With the ATLAS-based assay, similar amounts of CN-1 were detected in serum and both EDTA and heparin plasma. The RYSKS173-based assay detected CN-1 in serum in all individuals at significantly lower concentrations compared to the ATLAS-based assay (range: 0.1-1.8 vs. 1-50 μg/ml, RYSK- vs. ATLAS-based, P<0.01). CN-1 detection with the RYSK-based assay was increased in EDTA plasma, albeit at significantly lower concentrations compared to ATLAS. In heparin plasma, CN-1 was also poorly detected with the RYSK-based assay. Addition of DTT to serum increased the detection of CN-1 in the RYSK-based assay almost to the levels found in the ATLAS-based assay. Both ELISA assays were highly reproducible (R: 0.99, P<0.01 and R: 0.93, P<0.01, for the RYSK- and ATLAS-based assays, respectively). Results of the ATLAS-based assay showed a positive correlation with CN-1 activity (R: 0.62, P<0.01), while this was not the case for the RYSK-based assay. However, there was a negative correlation between CN-1 activity and the proportion of CN-1 detected in the RYSK-based assay, i.e., CN-1 detected with the RYSK-based assay/CN-1 detected with the ATLAS-based assay × 100% (Spearman-Rang correlation coefficient: -0.6, P<0.01), suggesting that the RYSK-based assay most likely detects a CN-1 conformation with low CN-1 activity. RYSK173 and ATLAS antibodies reacted similarly in Western blot, irrespective of PNGase treatment. Binding of RYSK173 in serum was not due to differential N-glycosylation as demonstrated by mutant CN-1 cDNA constructs. In conclusion, our study demonstrates a good correlation between enzyme activity and CN-1 protein concentration in ELISA and suggests the presence of different CN-1 conformations in serum. The relevance of these different conformations is still elusive and needs to be addressed in further studies.     

Mapping of binding site III in the leptin receptor and modeling of a hexameric leptin.leptin receptor complex

The leptin.leptin receptor (LR) system shows strong similarities to the long chain cytokine interleukin-6 (IL-6) and granulocyte colony-stimulating factor (G-CSF) cytokine.cytokine receptor systems. The IL-6 family cytokines interact with their receptors through three different binding sites (I-III). We demonstrated previously that leptin has similar binding sites I-III and mapped the interactions between binding site II and cytokine receptor homology domain II (CRH2) (Peelman, F., Van Beneden, K., Zabeau, L., Iserentant, H., Ulrichts, P., Defeau, D., Verhee, A., Catteeuw, D., Elewaut, D., and Tavernier, J. (2004) J. Biol. Chem. 279, 41038-41046). In this study, we built homology models for the CRH1 and Ig-like domains of the LR. The Ig-like domain shows a large conserved surface patch in the beta-sheet formed by beta-strands 3, 6, and 7. Mutations in this patch almost completely abolished the leptin-induced STAT3-dependent reporter activity. We propose that a conserved cluster of residues Leu370, Ala407, Tyr409, His417, and His418 forms the center of binding site III of the LR. We built a hexameric leptin.LR complex model based on the hexameric IL-6 complex. In this model, a conserved hydrophobic protuberance of Val36, Thr37, Phe41, and Phe43 in the A-B loop of leptin fits perfectly in the CRH2 domain, corresponding to the IL-6 alpha-receptor, and forms the center of binding site I. The 2:4 hexameric leptin.LR complex offers a rational explanation for mutagenesis studies and residue conservation.     

Coral biostromes of the Middle Jurassic from the Subbetic (Betic Cordillera,southern Spain): facies,coral taxonomy,taphonomy, and palaeoecology

Coral biostromes from the Camarena Formation (External Subbetic, Betic Cordillera) are reviewed under palaeoecologic, taphonomic, and palaeontologic aspects. The biostromes are dominated by phaceloid forms and are characterized by a typical shallow-marine microencruster assemblage with photophilic microencrusters and scarce microbial crusts. The abundance of stylinid corals and light-dependant microencrusters suggests oligotrophic conditions. Coral colonies were located among oolitic shoals that were unfavorable for coral growth. The corals were developed in phases without oolitic production alternating with phases of oolitic production, forming metric-scale sequences. A relative sea-level fall would have reduced the ooidal production and led to the deposition of thin layers of micritic facies in intertidal areas. The cementation and hardening of the bottom resulted in a hardground that was colonized by corals after a subsequent relative sea-level rise. The progressive increase of the energetic conditions induced an increasing production of ooids and the migration of oolitic shoals, which covered and finished the coral biostromes. Repetition of this process gave rise to sequences reflecting small pulses of oscillations in the relative sea level.     

Nitric oxide modulates ozone-induced cell death, hormone biosynthesis and gene expression in Arabidopsis thaliana

Nitric oxide (NO) is involved together with reactive oxygen species (ROS) in the activation of various stress responses in plants. We have used ozone (O₃) as a tool to elicit ROS-activated stress responses, and to activate cell death in plant leaves. Here, we have investigated the roles and interactions of ROS and NO in the induction and regulation of O₃-induced cell death. Treatment with O₃ induced a rapid accumulation of NO, which started from guard cells, spread to adjacent epidermal cells and eventually moved to mesophyll cells. During the later time points, NO production coincided with the formation of hypersensitive response (HR)-like lesions. The NO donor sodium nitroprusside (SNP) and O₃ individually induced a large set of defence-related genes; however, in a combined treatment SNP attenuated the O₃ induction of salicylic acid (SA) biosynthesis and other defence-related genes. Consistent with this, SNP treatment also decreased O₃-induced SA accumulation. The O₃-sensitive mutant rcd1 was found to be an NO overproducer; in contrast, Atnoa1/rif1 ( Arabidopsis nitric oxide associated 1/resistant to inhibition by FSM1 ), a mutant with decreased production of NO, was also O₃ sensitive. This, together with experiments combining O₃ and the NO donor SNP suggested that NO can modify signalling, hormone biosynthesis and gene expression in plants during O₃ exposure, and that a functional NO production is needed for a proper O₃ response. In summary, NO is an important signalling molecule in the response to O₃.     

Relevance of allosteric conformations and homocarnosine concentration on carnosinase activity

Activity of carnosinase (CN1), the only dipeptidase with substrate specificity for carnosine or homocarnosine, varies greatly between individuals but increases clearly and significantly with age. Surprisingly, the lower CN1 activity in children is not reflected by differences in CN1 protein concentrations. CN1 is present in different allosteric conformations in children and adults since all sera obtained from children but not from adults were positive in ELISA and addition of DTT to the latter sera increased OD450 values. There was no quantitative difference in the amount of monomeric CN1 between children and adults. Further, CN1 activity was dose dependently inhibited by homocarnosine. Addition of 80 μM homocarnosine lowered V _max for carnosine from 440 to 356 pmol/min/μg and increased K _m from 175 to 210 μM. The estimated K _i for homocarnosine was higher (240 μM). Homocarnosine inhibits carnosine degradation and high homocarnosine concentrations in cerebrospinal fluid (CSF) may explain the lower carnosine degradation in CSF compared to serum. Because CN1 is implicated in the susceptibility for diabetic nephropathy (DN), our findings may have clinical implications for the treatment of diabetic patients with a high risk to develop DN. Homocarnosine treatment can be expected to reduce CN1 activity toward carnosine, resulting in higher carnosine levels.