Search

Changes in the N-glycosylation of immunoglobulin G (IgG) are often observed in pathological states, such as autoimmune, inflammatory, neurodegenerative, cardiovascular diseases and some types of cancer. However, in most cases, it is not clear if the disease onset causes these changes, or if the changes in IgG N-glycosylation are among the risk factors for the diseases. The aim of this study was to investigate the casual relationships between IgG N-glycosylation traits and 12 diseases, in which the alterations of IgG N-glycome were previously reported, using two sample Mendelian randomization (MR) approach. We have performed two sample MR using publicly available summary statistics of genome-wide association studies of IgG N-glycosylation and disease risks. Our results indicate positive causal effect of systemic lupus erythematosus (SLE) on the abundance of N-glycans with bisecting N-acetylglucosamine in the total IgG N-glycome. Therefore, we suggest regarding this IgG glycosylation trait as a biomarker of SLE. We also emphasize the need for more powerful GWAS studies of IgG N-glycosylation to further elucidate the causal effect of IgG N-glycome on the diseases.

Human protein glycosylation is a complex process, and its in vivo regulation is poorly understood. Changes in glycosylation patterns are associated with many human diseases and conditions. Understanding the biological determinants of protein glycome provides a basis for future diagnostic and therapeutic applications. Genome-wide association studies (GWAS) allow to study biology via a hypothesis-free search of loci and genetic variants associated with a trait of interest. Sixteen loci were identified by three previous GWAS of human plasma proteome N-glycosylation. However, the possibility that some of these loci are false positives needs to be eliminated by replication studies, which have been limited so far. Here, we use the largest set of samples so far (4802 individuals) to replicate the previously identified loci. For all but one locus, the expected replication power exceeded 95%. Of the 16 loci reported previously, 15 were replicated in our study. For the remaining locus (near the KREMEN1 gene), the replication power was low, and hence, replication results were inconclusive. The very high replication rate highlights the general robustness of the GWAS findings as well as the high standards adopted by the community that studies genetic regulation of protein glycosylation. The 15 replicated loci present a good target for further functional studies. Among these, eight loci contain genes encoding glycosyltransferases: MGAT5, B3GAT1, FUT8, FUT6, ST6GAL1, B4GALT1, ST3GAL4 and MGAT3. The remaining seven loci offer starting points for further functional follow-up investigation into molecules and mechanisms that regulate human protein N-glycosylation in vivo.

Defining the genetic components that control glycosylation of the human immunoglobulin G (IgG) is an ongoing effort, which has so far been addressed by means of heritability, linkage and genome-wide association studies (GWAS). Unlike the synthesis of proteins, N-glycosylation biosynthesis is not a template-driven process, but rather a complex process regulated by both genetic and environmental factors. Current heritability studies have shown that while up to 75% of the variation in levels of some IgG glycan traits can be explained by genetics, some glycan traits are completely defined by environmental influences. Advances in both high-throughput genotyping and glycan quantification methods have enabled genome-wide association studies that are increasingly used to estimate associations of millions of single-nucleotide polymorphisms and glycosylation traits. Using this method, 18 genomic regions have so far been robustly associated with IgG N-glycosylation, discovering associations with genes encoding glycosyltransferases, but also transcription factors, co-factors, membrane transporters and other genes with no apparent role in IgG glycosylation. Further computational analyses have shown that IgG glycosylation is likely to be regulated through the expression of glycosyltransferases, but have also for the first time suggested which transcription factors are involved in the process. Moreover, it was also shown that IgG glycosylation and inflammatory diseases share common underlying causal genetic variants, suggesting that studying genetic regulation of IgG glycosylation helps not only to better understand this complex process but can also contribute to understanding why glycans are changed in disease. However, further studies are needed to unravel whether changes in IgG glycosylation are causing these diseases or the changes in the glycome are caused by the disease.

PURPOSE: Risk factors for chronic back pain (CBP) may share underlying genetic factors, making them difficult to study using conventional methods. We conducted a bi-directional Mendelian randomisation (MR) study to examine the causal effects of risk factors (education, smoking, alcohol consumption, physical activity, sleep and depression) on CBP and the causal effect of CBP on the same risk factors. METHODS: Genetic instruments for risk factors and CBP were obtained from the largest published genome-wide association studies (GWAS) of risk factor traits conducted in individuals of European ancestry. We used inverse weighted variance meta-analysis (IVW), Causal Analysis Using Summary Effect (CAUSE) and sensitivity analyses to examine evidence for causal associations. We interpreted exposure-outcome associations as being consistent with a causal relationship if results with IVW or CAUSE were statistically significant after accounting for multiple statistical testing (p

Summary Systemic lupus erythematosus (SLE) is characterized by a loss of self-tolerance, systemic inflammation, and multi-organ damage. While a variety of therapeutic interventions are available, it has become clear that an early diagnosis and treatment may be key to achieve long lasting therapeutic responses and to limit irreversible organ damage. Loss of humoral tolerance including the appearance of self-reactive antibodies can be detected years before the actual onset of the clinical autoimmune disease, representing a potential early point of intervention. Not much is known, however, about how and to what extent this pre-phase of disease impacts the onset and development of subsequent autoimmunity. By targeting the B cell compartment in the pre-disease phase of a spontaneous mouse model of SLE we now show, that resetting the humoral immune system during the clinically unapparent phase of the disease globally alters immune homeostasis delaying the downstream development of systemic autoimmunity., Graphical abstract, Highlights • The clinically unapparent pre-phase of SLE impacts clinical disease • Autoreactive IgM antibodies represent a biomarker for early therapeutic intervention • Pre-phase B cells orchestrate clinical disease • Depleting pre-phase B cells diminishes disease pathology, Biological sciences; Immune system disorder; Immune response.

Defining the genetic components that control glycosylation of the human immunoglobulin G (IgG) is an ongoing effort, which has so far been addressed by means of heritability, linkage and genome-wide association studies (GWAS). Unlike the synthesis of proteins, N-glycosylation biosynthesis is not a template-driven process, but rather a complex process regulated by both genetic and environmental factors. Current heritability studies have shown that while up to 75% of the variation in levels of some IgG glycan traits can be explained by genetics, some glycan traits are completely defined by environmental influences. Advances in both high-throughput genotyping and glycan quantification methods have enabled genome-wide association studies that are increasingly used to estimate associations of millions of single-nucleotide polymorphisms and glycosylation traits. Using this method, 18 genomic regions have so far been robustly associated with IgG N-glycosylation, discovering associations with genes encoding glycosyltransferases, but also transcription factors, co-factors, membrane transporters and other genes with no apparent role in IgG glycosylation. Further computational analyses have shown that IgG glycosylation is likely to be regulated through the expression of glycosyltransferases, but have also for the first time suggested which transcription factors are involved in the process. Moreover, it was also shown that IgG glycosylation and inflammatory diseases share common underlying causal genetic variants, suggesting that studying genetic regulation of IgG glycosylation helps not only to better understand this complex process but can also contribute to understanding why glycans are changed in disease. However, further studies are needed to unravel whether changes in IgG glycosylation are causing these diseases or the changes in the glycome are caused by the disease.

The N-glycosylation of immunoglobulin G (IgG) affects its structure and function. It has been demonstrated that IgG N-glycosylation patterns are inherited as complex quantitative traits. Genome-wide association studies identified loci harboring genes encoding enzymes directly involved in protein glycosylation as well as loci likely to be involved in regulation of glycosylation biochemical pathways. Many of these loci could be linked to immune functions and risk of inflammatory and autoimmune diseases. The aim of the present study was to discover and replicate new loci associated with IgG N-glycosylation and to investigate possible pleiotropic effects of these loci onto immune function and the risk of inflammatory and autoimmune diseases. We conducted a multivariate genome-wide association analysis of 23 IgG N-glycosylation traits measured in 8090 individuals of European ancestry. The discovery stage was followed up by replication in 3147 people and in silico functional analysis. Our study increased the total number of replicated loci from 22 to 29. For the discovered loci, we suggest a number of genes potentially involved in the control of IgG N-glycosylation. Among the new loci, two (near RNF168 and TNFRSF13B) were previously implicated in rare immune deficiencies and were associated with levels of circulating immunoglobulins. For one new locus (near AP5B1/OVOL1), we demonstrated a potential pleiotropic effect on the risk of asthma. Our findings underline an important link between IgG N-glycosylation and immune function and provide new clues to understanding their interplay.

The N-glycosylation profile of total human plasma proteins could be a useful biomarker for various pathological states. Reliable high- throughput methods for such profiling have been developed. However, studies of relative importance of genetic and environmental factors in regulating plasma N-glycome are scarce. The aim of our study was to determine the role of genetic factors in phenotypic variation of plasma N-glycan profile through the estimates of its heritability. Thirty-nine total plasma N-glycome traits were analyzed in 2816 individuals from the TwinsUK data set. For the majority of the traits, high heritability estimates (>50%) were obtained pointing at a significant contribution of genetic factors in plasma N-glycome variation, especially for glycans mostly attached to immunoglobulins. We have also found several structures with higher environmental contribution to their variation.

Background Genome-wide association studies have identified hundreds of loci that influence a wide variety of complex human traits; however, little is known regarding the biological mechanism of action of these loci. The recent accumulation of functional genomics (“omics”), including metabolomics data, has created new opportunities for studying the functional role of specific changes in the genome. Functional genomic data are characterized by their high dimensionality, the presence of (strong) statistical dependency between traits, and, potentially, complex genetic control. Therefore, the analysis of such data requires specific statistical genetics methods. Results To facilitate our understanding of the genetic control of omics phenotypes, we propose a trait-centered, network-based conditional genetic association (cGAS) approach for identifying the direct effects of genetic variants on omics-based traits. For each trait of interest, we selected from a biological network a set of other traits to be used as covariates in the cGAS. The network can be reconstructed either from biological pathway databases (a mechanistic approach) or directly from the data, using a Gaussian graphical model applied to the metabolome (a data-driven approach). We derived mathematical expressions that allow comparison of the power of univariate analyses with conditional genetic association analyses. We then tested our approach using data from a population-based Cooperative Health Research in the region of Augsburg (KORA) study (n = 1,784 subjects, 1.7 million single-nucleotide polymorphisms) with measured data for 151 metabolites. Conclusions We found that compared to single-trait analysis, performing a genetic association analysis that includes biologically relevant covariates can either gain or lose power, depending on specific pleiotropic scenarios, for which we provide empirical examples. In the context of analyzed metabolomics data, the mechanistic network approach had more power compared to the data-driven approach. Nevertheless, we believe that our analysis shows that neither a prior-knowledge-only approach nor a phenotypic-data-only approach is optimal, and we discuss possibilities for improvement.

Immunoglobulin G (IgG) is the most abundant immunoglobulin isotype in the blood and is involved in the pathogenesis and progression of various diseases. Glycosylation of the IgG fragment crystallizable (Fc) region is shown to vary in different physiological and pathological states. Fc N-glycan composition can alter the effector functions of IgG by modulating its affinity for ligands, such as Fcγ receptors (FcγRs). However, it is not known whether IgG glycosylation is affected by the available repertoire of FcγRs, and if the Fc-linked N-glycome can compensate for modulation of the IgG–FcγR interaction. To explore this, we examined the subclass-specific Fc IgG glycoprofiles of healthy male and female FcγR knock-out mice on C57BL/6 and BALB/c backgrounds. We observed slight changes in IgG Fc N-glycan profiles in different knock-outs; however, it seems that the strain background and sex have a stronger effect on N-glycosylation of IgG Fc regions than the FcγR repertoire.

The

The plastidic psbA-trnH spacer was sequenced in 78 accessions representing the genus Pisum L. A nucleotide substitution C64T in the spacer was found to be specific for Pisum abyssinicum A. Br.; a substitution T75G occurred in five accessions of the wild pea [Pisum sativum L. subsp. elatius (Bieb.) Schmalh.], one from France, three from Greece and one from Turkey, and also in a primitive landrace from Afghanistan with signs of contamination. A 7-bp tandem duplication was found in two P. sativum subsp. elatius accessions, from Turkey and Georgia. All (except for the probably contaminated Afghan accession) of the cultivated subspecies P. sativum L. subsp. sativum had a deletion of one copy of a tandem 8-bp repeat. The same deletion was found in two wild pea (P. sativum subsp. elatius) accessions, from Bulgaria and Georgia, belonging to the same earlier defined lineage B as the cultivated pea. They are supposed to represent the ancestral evolutionary lineage of the cultivated pea. It is noteworthy that accessions from the proposed Core Area of the Near East founder crop domestication in south-eastern Turkey do not have the deletion. Most of wild representatives of lineage B have a scarcely pigmented flower with almost non-opening standard, but those from Transcaucasia have a normal flower. The revealed area of co-existence of the earlier defined wild pea lineages A and B (differing in alleles of three molecular markers from different cellular genomes) was extended from Turkey to include Georgia and probably North Africa. Accessions claimed to represent wild peas were tested for spontaneous pod dehiscing and 14 of them were disproved as such. They are enriched with ‘recombinant’ marker combinations and most probably resulted from hybridisation of wild and cultivated peas, either in nature or while reproducing in germplasm collections.

Background: Genome-wide association studies have identified hundreds of loci that influence a wide variety of complex human traits; however, little is known regarding the biological mechanism of action of these loci. The recent accumulation of functional genomics ("omics"), including metabolomics data, has created new opportunities for studying the functional role of specific changes in the genome. Functional genomic data are characterized by their high dimensionality, the presence of (strong) statistical dependency between traits, and, potentially, complex genetic control. Therefore, the analysis of such data requires specific statistical genetics methods. Results: To facilitate our understanding of the genetic control of omics phenotypes, we propose a trait-centered, network-based conditional genetic association (cGAS) approach for identifying the direct effects of genetic variants on omics-based traits. For each trait of interest, we selected from a biological network a set of other traits to be used as covariates in the cGAS. The network can be reconstructed either from biological pathway databases (a mechanistic approach) or directly from the data, using a Gaussian graphical model applied to the metabolome (a data-driven approach). We derived mathematical expressions that allow comparison of the power of univariate analyses with conditional genetic association analyses. We then tested our approach using data from a population-based Cooperative Health Research in the region of Augsburg (KORA) study (n = 1,784 subjects, 1.7 million single-nucleotide polymorphisms) with measured data for 151 metabolites. Conclusions: We found that compared to single-trait analysis, performing a genetic association analysis that includes biologically relevant covariates can either gain or lose power, depending on specific pleiotropic scenarios, for which we provide empirical examples. In the context of analyzed metabolomics data, the mechanistic network approach had more power compared to the data-driven approach. Nevertheless, we believe that our analysis shows that neither a prior-knowledge-only approach nor a phenotypic-data-only approach is optimal, and we discuss possibilities for improvement.

Immunoglobulin G (IgG) glycosylation is essential for function of the immune system, but the genetic and environmental factors that underlie its inter- individual variability are not well defined. The Collaborative Cross (CC) genetic resource harnesses over 90% of the common genetic variation of the mouse. By analyzing the IgG glycome composition of 95 CC strains, we made several important observations: (i) glycome variation between mouse strains was higher than between individual humans, despite all mice having the same environmental influences ; (ii) five genetic loci were found to be associated with murine IgG glycosylation ; (iii) variants outside traditional glycosylation site motifs affected glycome variation ; (iv) bisecting N- acetylglucosamine (GlcNAc) was produced by several strains although most previous studies have reported the absence of glycans containing the bisecting GlcNAc on murine IgGs ; and (v) common laboratory mouse strains are not optimal animal models for studying effects of glycosylation on IgG function.

Immunoglobulin G (IgG) N-glycosylation is crucial for its effector functions. It is a complex trait, and large sample sets are needed to discover multiple genetic factors that underlie it. While in humans such high-throughput studies of IgG N-glycans became usual, only one has been carried out in mice. Here we describe and validate a method for the relative quantification of IgG Fc-linked N-glycans in a subclass-specific manner using nano-reverse phase liquid chromatography coupled with mass-spectrometry (nanoRP-LC-MS) applied to murine IgG. High-throughput data processing is ensured by the LaCyTools software. We have shown that IgG isolation procedure is the main source of technical variation in the current protocol. The major glycoforms were quantified reliably with coefficients of variation below 6% for all the analytes with relative abundances above 5%. We have applied our method to a sample set of 3 inbred strains: BALB/c, C57BL/6 and C3H and observed differences in subclass-specific and strain-specific N-glycosylation of IgG, suggesting a significant genetic component in the regulation of Fc-linked IgG N-glycosylation.

Two histone H1 subtype genes, His7 and His5 , were sequenced in a set of 56 pea accessions. Phylogenetic reconstruction based on concatenated His5 and His7 sequences had three main clades. First clade corresponded to Pisum fulvum , the next divergence separated a clade inside Pisum sativum in the broad sense that did not correspond strictly to any proposed taxonomical subdivisions. According to our estimations, the earliest divergence separating P. fulvum occurred 1.7 ± 0.4 MYA. The other divergence with high bootstrap support that separated two P. sativum groups took place approximately 1.3 ± 0.3 MYA. Thus, the main divergences in the genus took place either in late Pliocene or in early Pleistocene, the time of onset of the profound climate cooling in the northern hemisphere. The ω = K(a) / K(s) ratio was 2.5 times higher for His5 sequences than for His7 . Thus, His7 gene, coding for a unique subtype specific for actively growing tissues, might have evolved under stricter evolutionary constraints than His5 , that codes for a minor H1 subtype with less specific expression pattern. For this reason phylogenetic reconstructions separately obtained from His5 sequences resolved tree topology much better than those obtained from His7 sequences. Computational estimation of population dynamic parameters in the genus Pisum L. from His5 - His7 sequences using IMa2 software revealed a decrease of effective population size on the early stage of Pisum evolution.

This work has started as a project for the summer school in molecular and theoretical biology. Efficient teaching and learning modern methods of molecular biology and population genetics in a two-week study course for high school students needs an attractive subject. We chose phylogeography of the domestic cat as the subject of our project because 1) everybody likes cats; 2) cats are polymorphic for several coat color mutations, which can be easily detected by street survey or in the photographs; 3) samples for DNA extraction are easy to collect without posing ethical and biosafety problems; 4) rich background information of geographical distribution of coat color alleles and variation in mtDNA is available; 5) phylogeography of cat population in Russia is poorly studied and therefore new data collected in this large territory may shed a light on the global cat distribution and on the origin of the fancy breeds.During the project students studied coat color and mitotype distribution across Russian random bred cats. The basics of field (observations of natural cat populations, hair collection), formal (inheritance of coat colors), mathematical population (allele frequency distributions and their comparisons, building phylogenetic trees, multidimensional scaling), and molecular (DNA extraction and amplification, quality control of DNA sequencing data) genetics were covered.We scored coat color phenotypes in 1182 cats and sequenced mtDNA control region from hair samples of 38 cats from 18 geographical sites. Analysis of coat color alleles frequencies and mitotype distribution confirmed relative homogeneity of gene pools of Russian cat populations, indicating their recent origin. We found several unique mitotypes and demonstrated that OL1 mitotype, previously found only in Siberian fancy breed, was present in random bred cats from several Russian cities. This contributes to the discussion on the origin of the Siberian breed of cats, and supports the view that Siberians is a recent breed created in the 1980s by breeding of selected representatives of the random bred population.

BackgroundGenome-wide association studies (GWAS) have identified hundreds of loci influencing complex human traits, however, their biological mechanism of action remains mostly unknown. Recent accumulation of functional genomics (‘omics’) including metabolomics data opens up opportunities to provide a new insight into the functional role of specific changes in the genome. Functional genomic data are characterized by high dimensionality, presence of (strong) statistical dependencies between traits, and, potentially, complex genetic control. Therefore, analysis of such data asks for development of specific statistical genetic methods.ResultsWe propose a network-based, conditional approach to evaluate the impact of genetic variants on omics phenotypes (conditional GWAS, cGWAS). For each trait of interest, based on biological network, we select a set of other traits to be used as covariates in GWAS. The network could be reconstructed either from biological pathway databases or directly from the data. We evaluated our approach using data from a population-based KORA study (n=1,784, 1.7 M SNPs) with measured metabolomics data (151 metabolites) and demonstrated that our approach allows for identification of up to five additional loci not detected by conventional GWAS. We show that this gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific ‘contra-intuitive’ pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.ConclusionsWe demonstrate that in context of metabolomics network-based, conditional genome-wide association analysis is able to dramatically increase power of identification of loci with specific ‘contra-intuitive’ pleiotropic architecture. Our method has modest computational costs, can utilize summary level GWAS data, and is applicable to other omics data types. We anticipate that application of our method to new and existing data sets will facilitate progress in understanding genetic bases of control of molecular and complex phenotypes.Short abstractWe propose a network-based, conditional approach for genome-wide analysis of multivariate omics phenotypes. Our methods can incorporate prior biological knowledge about biological pathways from external sources. We evaluated our approach using metabolomics data and demonstrated that our approach has bigger power and allows for identification of additional loci. We show that gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific ‘contra-intuitive’ pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.

A number of alleles of an orthologous gene His6 encoding histone H1 subtype f (H1-6 in pea) accumulated in chromatin of old tissues were sequenced in three legume species: seven alleles in Pisum sativum, four in Vicia unijuga and eight in Lathyrus gmelinii. In the total of 19 alleles sequenced in the three species, 29 non-synonymous substitutions and six indels were found in the coding region; most of amino acid substitutions (26 of 29) and all indels occurred in the C-terminal hydrophilic domain of the encoded protein. All species were polymorphic for some non-synonymous substitutions, V. unijuga was also polymorphic for one and P. sativum for two indels. Three near-isogenic lines of P. sativum bearing different alleles showed differences in many quantitative traits; that in the growth dynamic could be tentatively attributed to the allelic substitution of subtype H1-6. The frequencies of four electromorphs in a sampled locality of V. unijuga were found to be close to those observed 25 years ago, although their rapid change in the past was supposed in the previous study.

A phylogenetic analysis of the genus Pisum (peas), embracing diverse wild and cultivated forms, which evoke problems with species delimitation, was carried out based on a gene coding for histone H1, a protein that has a long and variable functional C-terminal domain. Phylogenetic trees were reconstructed on the basis of the coding sequence of the gene His5 of H1 subtype 5 in 65 pea accessions. Early separation of a clear-cut wild species Pisum fulvum is well supported, while cultivated species Pisum abyssinicum appears as a small branch within Pisum sativum. Another robust branch within P. sativum includes some wild and almost all cultivated representatives of P. sativum. Other wild representatives form diverse but rather subtle branches. In a subset of accessions, PsbA-trnH chloroplast intergenic spacer was also analysed and found less informative than His5. A number of accessions of cultivated peas from remote regions have a His5 allele of identical sequence, encoding an electrophoretically slow protein product, which earlier attracted attention as likely positively selected in harsh climate conditions. In PsbA-trnH, a 8 bp deletion was found, which marks cultivated representatives of P. sativum.

Twelve accessions classified as Pisum sativum subsp. elatius, mostly from West and Central Mediterranean, were analysed for three markers from different cellular genomes: rbcL (plastid genome), coxI (mitochondrial genome) and SCA (nuclear genome). Based on geographical distribution of their allele combinations analysed in this and the earlier study, we suggest a putative history of wild representatives of P. sativum. The ancestor of this species belonged to lineage A (coxI+, rbcL+, SCAf); it appeared in East Mediterranean, then spread westward most probably during one of the Pleistocene coolings when the sea was smaller, so that representatives of lineage A remained in the Eastern Mediterranean and on the islands of Sicily and Menorca. Mutation leading to the loss of the restriction site for PsiI in coxI−, gave rise to lineage C (coxI−, rbcL+, SCAf) which spread widely in the Mediterranean and is now found in France, Greece and Ethiopia. Mutation leading to rbcL− gave rise to lineage D (coxI−, rbcL−, SCAf), now found in Egypt (P. sativum subsp. jomardii) and Spain. Mutational transition of SCAf to SCAs most probably took place in North-Eastern Mediterranean since the resulting lineage B (coxI−, rbcL−, SCAs) now occupies the Tauro-Caucasian area. In Asia Minor and North Israel, line B met the ancestral line A so that both lines coexist there presently. The lineage B gave rise to the cultivated P. sativum subsp. sativum.

Much of the research on insulators in Drosophila has been done with transgenic constructs using the white gene (mini-white) as reporter. Hereby we report that the sequence between the white and CG32795 genes in Drosophila melanogaster contains an insulator of a novel kind. Its functional core is within a 368 bp segment almost contiguous to the white 3′UTR, hence we name it as Wari (white-abutting resident insulator). Though Wari contains no binding sites for known insulator proteins and does not require Su(Hw) or Mod(mdg4) for its activity, it can equally well interact with another copy of Wari and with unrelated Su(Hw)-dependent insulators, gypsy or 1A2. In its natural downstream position, Wari reinforces enhancer blocking by any of the three insulators placed between the enhancer and the promoter; again, Wari–Wari, Wari–gypsy or 1A2–Wari pairing results in mutual neutralization (insulator bypass) when they precede the promoter. The distressing issue is that this element hides in all mini-white constructs employed worldwide to study various insulators and other regulatory elements as well as long-range genomic interactions, and its versatile effects could have seriously influenced the results and conclusions of many works.

In crosses of wild and cultivated peas (Pisum sativum L.), nuclear-cytoplasmic incompatibility frequently occurs manifested as decreased pollen fertility, male gametophyte lethality, sporophyte lethality. High-throughput sequencing of plastid genomes of one cultivated and four wild pea accessions differing in cross-compatibility was performed. Candidate genes for involvement in the nuclear-plastid conflict were searched in the reconstructed plastid genomes. In the annotated Medicago truncatula genome, nuclear candidate genes were searched in the portion syntenic to the pea chromosome region known to harbor a locus involved in the conflict. In the plastid genomes, a substantial variability of the accD locus represented by nucleotide substitutions and indels was found to correspond to the pattern of cross-compatibility among the accessions analyzed. Amino acid substitutions in the polypeptides encoded by the alleles of a nuclear locus, designated as Bccp3, with a complementary function to accD, fitted the compatibility pattern. The accD locus in the plastid genome encoding beta subunit of the carboxyltransferase of acetyl-coA carboxylase and the nuclear locus Bccp3 encoding biotin carboxyl carrier protein of the same multi-subunit enzyme were nominated as candidate genes for main contribution to nuclear-cytoplasmic incompatibility in peas. Existence of another nuclear locus involved in the accD-mediated conflict is hypothesized.