Svetlana Vinogradova

Draft Genome Sequence of Xanthomonas arboricola Strain 3004, a Causal Agent of Bacterial Disease on Barley

ABSTRACT We report here the annotated genome sequence of Xanthomonas arboricola strain 3004, isolated from barley leaves with symptoms of streak and capable of infecting other plant species. We sequenced the genome of X. arboricola strain 3004 to improve the understanding of molecular mechanisms of the pathogenesis and evolution of the genus Xanthomonas.

RNASurface: fast and accurate detection of locally optimal potentially structured RNA segments

MOTIVATIONnDuring the past decade, new classes of non-coding RNAs (ncRNAs) and their unexpected functions were discovered. Stable secondary structure is the key feature of many non-coding RNAs. Taking into account huge amounts of genomic data, development of computational methods to survey genomes for structured RNAs remains an actual problem, especially when homologous sequences are not available for comparative analysis. Existing programs scan genomes with a fixed window by efficiently constructing a matrix of RNA minimum free energies. A wide range of lengths of structured RNAs necessitates the use of many different window lengths that substantially increases the output size and computational efforts.nnnRESULTSnIn this article, we present an algorithm RNASurface to efficiently scan genomes by constructing a matrix of significance of RNA secondary structures and to identify all locally optimal structured RNA segments up to a predefined size. RNASurface significantly improves precision of identification of known ncRNA in Bacillus subtilis.nnnAVAILABILITY AND IMPLEMENTATIONnRNASurface C source code is available from http://bioinf.fbb.msu.ru/RNASurface/downloads.html.

Risk alleles of genes with monoallelic expression are enriched in gain-of-function variants and depleted in loss-of-function variants for neurodevelopmental disorders

Over 3000 human genes can be expressed from a single allele in one cell, and from the other allele—or both—in neighboring cells. Little is known about the consequences of this epigenetic phenomenon, monoallelic expression (MAE). We hypothesized that MAE increases expression variability, with a potential impact on human disease. Here, we use a chromatin signature to infer MAE for genes in lymphoblastoid cell lines and human fetal brain tissue. We confirm that across clones MAE status correlates with expression level, and that in human tissue data sets, MAE genes show increased expression variability. We then compare mono- and biallelic genes at three distinct scales. In the human population, we observe that genes with polymorphisms influencing expression variance are more likely to be MAE (P<1.1 × 10−6). At the trans-species level, we find gene expression differences and directional selection between humans and chimpanzees more common among MAE genes (P<0.05). Extending to human disease, we show that MAE genes are under-represented in neurodevelopmental copy number variants (CNVs) (P<2.2 × 10−10), suggesting that pathogenic variants acting via expression level are less likely to involve MAE genes. Using neuropsychiatric single-nucleotide polymorphism (SNP) and single-nucleotide variant (SNV) data, we see that genes with pathogenic expression-altering or loss-of-function variants are less likely MAE (P<7.5 × 10−11) and genes with only missense or gain-of-function variants are more likely MAE (P<1.4 × 10−6). Together, our results suggest that MAE genes tolerate a greater range of expression level than biallelic expression (BAE) genes, and this information may be useful in prediction of pathogenicity.

Probing-directed identification of novel structured RNAs

ABSTRACT Transcripts often harbor RNA elements, which regulate cell processes co- or post-transcriptionally. The functions of many regulatory RNA elements depend on their structure, thus it is important to determine the structure as well as to scan genomes for structured elements. State of the art ab initio approaches to predict structured RNAs rely on DNA sequence analysis. They use 2 major types of information inferred from a sequence: thermodynamic stability of an RNA structure and evolutionary footprints of base-pair interactions. In recent years, chemical probing of RNA has arisen as an alternative source of structural information. RNA probing experiments detect positions accessible to specific types of chemicals or enzymes indicating their propensity to be in a paired or unpaired state. There exist several strategies to integrate probing data into RNA secondary structure prediction algorithms that substantially improve the prediction quality. However, whether and how probing data could contribute to detection of structured RNAs remains an open question. We previously developed the energy-based approach RNASurface to detect locally optimal structured RNA elements. Here, we integrate probing data into the RNASurface energy model using a general framework. We show that the use of experimental data allows for better discrimination of ncRNAs from other transcripts. Application of RNASurface to genome-wide analysis of the human transcriptome with PARS data identifies previously undetectable segments, with evidence of functionality for some of them.

Genome-wide search for functional noncoding RNA

Noncoding RNAs (ncRNAs) are functional transcripts that do not encode proteins; they are involved in many regulation pathways. The only common feature shared by most (but not all) known ncRNAs is their ability to fold into secondary structures that are crucial for their functioning and thus should be conserved. This fact can be used for genome-wide prediction of ncRNAs. The approach employed in this study was based on computing local base pairing probabilities and further maximizing the total pairing probability for a segment using the Nussinoff-like algorithm. The suggested method was shown to efficiently predict known ncRNAs and possibly some new ncRNAs.

Genetic diversity of turnip mosaic virus and the mechanism of its transmission by Brassica seeds.

I8, and I10) characteristic of viruses isolated from 87 samples of cultivated plants of the genus Brassica that had the symptoms of turnip mosaic in Moscow region were classified by the signs of lesion of host plants, response of indicator plants, and results of ELISA. For all the isolates, three taxonomically informative genes P1, Nib, and CP were partly sequenced. Phylogenetic analysis showed that they belonged to two phylogee netic groups with cDNA similarity of 95%. According to preliminary information, one group of virus isolates was brought to Russia with the seed collection of Brass sica rapa L. varieties. It was demonstrated that the TuMV I2 isolate is transmitted by seeds of TuMVVsuss ceptible and TuMVVtolerant plants of the genus Brass sica at a frequency of up to 40%, and the mechanism of the virus transmission through infection of the embryo was shown. The analysis of the obtained genomic cDNA sequence of the TuMV isolate I2 showed that its ability to be transmitted by seeds may be associated with amino acid substitutions in the P1 gene sequence. Turnip mosaic virus (TuMV) infects a wide range of cultivated, wild, and weedy species of 156 genera belonging to 43 families of plants, including all cultii vated species of the family Brassicaceae [1] and causes significant economic losses during the cultivation of crops of the species Brassica [2]. This is the only memm ber of the genus Potyvirus infecting plants of the family Brassicaceae [3]. Two TuMV isolates from Russia were included by foreign researchers for comparison of the virus genetic polymorphism; however, the diversity of the Russian population of the virus has to be investigated. The group of potiviruses includes 179 species, of which less than 10% are transmitted with seeds at a frequency of 1 to 80%. Only one case of TuMV detection in Raphaa nus raphanistrum seeds with a frequency of 4% was described in the literature [4]. However, the authors [4] have not demonstrated the fact of infection of the new generation of plants; for this reason, the absence of transmission of this virus with plant seeds is acknowledged. It is believe that this virus is transmitt ted only by insect vectors, primarily aphids of the genn era Macrosiphum or Myzus [1]. This study was performed on plant samples with the symptoms of TuMV infection, which were collected in different areas of Moscow region. To identify and conn firm …

High prevalence of clonal monoallelic expression

To the Editor — In recent years, there has been substantial interest in clonally stable monoallelic expression of autosomal genes in mammalian cells. This ‘autosomal analog of X-chromosome inactivation’ has been observed to affect hundreds of genes in a variety of cell types (overview in ref. 1). These genes tend to encode cell-surface proteins2,3 and to show high heterozygosity in human populations4. This observation has intriguing implications regarding the role of monoallelic expression in biological variation, especially if such genes are abundant. In a recent paper5, Sandberg and colleagues have proposed that transient monoallelic expression due to transcriptional bursts is abundant, whereas clonally stable monoallelic expression (random monoclonal expression of autosomal genes (aRME), a term also used herein) is “surprisingly scarce (< 1% of genes).” They go on to state that their observations “[call] into question the notion of widespread clonal aRME affecting thousands of genes”1–3,6, suggesting a sharp contrast to previous work from several groups, including ours. Upon careful analysis, we argue that that the findings that they report are consistent with the literature on aRME, and apparent discrepancies are due to issues with either semantics or simple methodological choices. It is outside the scope of our comments to discuss complex technical issues involved in allele-specific analysis of single-cell RNAsequencing data. Thus, we will take the factual findings by Sandberg and colleagues as reported and will focus on how these findings relate to previous claims regarding clonal aRME (recent overview in ref. 7). First, we examine the meaning of ‘prevalence’ in the context of aRME. On the one hand, prevalence could refer to the number of aRME genes per clone, which can be relatively low. On the other hand, it could apply to the number of genes in the genome that are subject to this mode of regulation, which is much greater. In each individual clone, relatively few genes are classified as monoallelically expressed, and the same genes can be stably biallelically expressed in other clones. When multiple clones are assessed, however, the cumulative number of genes exhibiting aRME reaches into the hundreds. For example, in the first genomewide analysis of aRME in human cells, we used SNP-array analysis in bulk clonal cell populations to identify 30–50 genes with monoallelic expression per lymphoblast clone, with a total of approximately 400 observed across 12 clones2. We and other groups have also used RNA-seq analysis in bulk clonal populations of different mouse cell types (refs. 8–10 among others). These studies were designed to identify clonally stable aRME, which is consistent across most cells of a given clone, but would not detect transient monoallelic expression, which varies. By applying to these studies the same 98:2 allelic bias cutoff used by Sandberg and colleagues5, we found 362 monoallelic genes per clone in mouse B cells (701 over two clones)9, 178 per fibroblast clone (330 over two clones)9, and 301 per neuronal progenitor clone (1,079 genes over eight clones)10. These numbers are in line with the observations of clonal aRME by Sandberg and colleagues, especially considering the challenges in detecting the allelic bias of all but the highest expressing genes in single cells11. In their study5, Sandberg and colleagues reported 41 and 47 clonally stable aRME genes in two fibroblast clones, and five of these genes showed aRME in both clones. A straightforward extrapolation from these numbers (with a 10–15% probability that an aRME gene that is monoallelic in one clone would also show monoallelic expression in the second clone) would bring the total estimated number of aRME genes in fibroblasts to 300–400; these genes should be detected if a sufficiently large number of clones were analyzed. In addition, the reported overlap of aRME genes between the two clones suggests (on the basis of a simple binomial model) that as few as 10–20 independent clones may be sufficient to identify most informative aRME genes. The estimate of the potential genomewide prevalence of clonal aRME may be even higher, given that such genes show highly cell-type-specific expression8,9. Thus, a union over multiple cell types would cumulatively reach ~30% of all protein-coding genes in humans and mice2,4,9, as we have reported before by using a different approach. The second issue leading to apparent discrepancies in the numbers of aRME genes arises from a straightforward question of methodology: the number of genes classified as aRME will obviously strongly depend on the allelic bias threshold used in analysis. The choice of the 98% threshold by Sandberg and colleagues appears appropriate given the challenges of single-cell RNA-seq, which make confident detection of less extreme bias difficult11. However, given that this issue is one of measurement, the threshold imposed is by necessity arbitrary. Sandberg and colleagues advocate for a more stringent threshold; whether that threshold is functionally relevant is unclear. For instance, there is no biological reason to expect a dramatic functional difference between ‘monoallelic’ expression with a 98:2 allelic bias compared to a 97:3 bias. Thus, in settings that allow for more precise measurement of bias, the rationale for choosing any particular threshold depends on the biological question asked. We and other groups have often used more permissive thresholds, which are robust in bulk RNA-seq analysis. For instance, an RNA-seq study of neuronal lineage cells10 has applied an 85% allelic bias threshold and reported up to 2,444 genes with clonal monoallelic expression across eight clones. Defining larger, more inclusive sets of aRME genes allows the sets to be interrogated in genome-wide analyses, thus yielding new biological insights. For example, we have recently found that in neurodevelopmental disease, point mutations, but not copy number variants, are linked to pathology12. We have also reported an unexpected observation that this group of genes has been subject to large-scale balancing selection4. After carefully considering the definitions of aRME, we believe that the findings on clonally stable aRME reported by Sandberg and colleagues using single-cell analysis confirm, rather than call into question, previous analyses performed in bulk clonal cell populations. These findings all suggest that aRME is a phenomenon that affects a large fraction of genes in the genome. ❐

Comprehensive analysis of draft genomes of two closely related pseudomonas syringae phylogroup 2b strains infecting mono- and dicotyledon host plants

BackgroundIn recent years, the damage caused by bacterial pathogens to major crops has been increasing worldwide. Pseudomonas syringae is a widespread bacterial species that infects almost all major crops. Different P. syringae strains use a wide range of biochemical mechanisms, including phytotoxins and effectors of the type III and type IV secretion systems, which determine the specific nature of the pathogen virulence.ResultsStrains 1845 (isolated from dicots) and 2507 (isolated from monocots) were selected for sequencing because they specialize on different groups of plants. We compared virulence factors in these and other available genomes of phylogroup 2 to find genes responsible for the specialization of bacteria. We showed that strain 1845 belongs to the clonal group that has been infecting monocots in Russia and USA for a long time (at least 50xa0years). Strain 1845 has relatively recently changed its host plant to dicots.ConclusionsThe results obtained by comparing the strain 1845 genome with the genomes of bacteria infecting monocots can help to identify the genes that define specific nature of the virulence of P. syringae strains.

Prediction of long-term treatment outcome in HCV following 24 day PEG-IFN alpha-2b therapy using population pharmacokinetic-pharmacodynamic mixture modeling and classification analysis.

Mathematical models have been widely used for understanding the dynamics of the hepatitis C virus (HCV). We propose a method to predict final clinical outcome for 24 HIV-HCV - coinfected patients with the help of a mathematical model based on the first two weeks of PEG-IFN therapy. Applying a pharmacokinetic-pharmacodynamic (PKPD) approach, together with mixture models, to the adapted model of viral dynamics developed by Neumann et al., we have analyzed the influence of PEG-IFN on the kinetics and interaction of target cells, infected cells and virus mRNA. It was found that PEG-IFN pharmacokinetic parameters were similar in sustained virological responders and nonresponders, while the plasma PEG-IFN concentration that decreases HCV production by 50% (EC50) and the rate of infected cell death were different. The treatment outcome depended mainly on the initial viral mRNA concentration and the rate of infected cell death. The population PKPD approach with a mixture model enabled the determination of individual PKPD parameters and showed high sensitivity (93.5%) and specificity (97.4%) for the prediction of the treatment outcome.

Expression of beet yellows virus coat protein cDNA to create transgenic resistance in plants.

68 The induction of transgenic resistance to the beet yellows virus (BYV) was studied using the cDNA of the BYV coat protein gene. For expression in plants, the pBI_BYVcp binary vector was constructed, in which the cDNA of the BYV coat protein gene was under the control of the 35S promoter and the NOS terminator. Using Agrobacteriummediated transformation, transs genic Nicotiana benthamiana plants were obtained, which were tested for the presence and expression of the target insertion by PCR, RTTPCR, and immunoo chromatography. As a result, a homozygous Т 2 generr ation was obtained, in which the stable expression of the target insert was confirmed. In modern agriculture, one of the factors reducing crop yields and product quality are viral diseases of plants. The sugar beet (Beta vulgaris L.) culture is affected by several serious viral infections. The beet yellows virus (BYV), belonging to the Closteroviridae family, is one of the viruses that induce yellowing and stunting of sugar beet. Of all the known viral pathoo gens, BYV causes the most significant losses of sugar beet yield. In early infection in the field, the sugar conn tent may decrease by 47% [1]. BYV is widely spread in the areas of sugarrbeet cultivation in the world, Balkan countries, and Ukraine. BYV epidemics were registered in the United States and Japan. The symptoms of BYV were also reported in Russia, and BYV should be considered a potentially dangerous disease of sugar beet for our country. BYV is transferred by 23 species of aphids and affects many species of weeds, which serve as reservoirs of the infecc tion [2]. BYV infection is extremely difficult to control by agronomic and chemical methods of plant protecc tion. For the first time, the technology of virus coat proo tein (CP) gene expression was used to induce resiss tance to tobacco mosaic virus in transgenic tobacco plants [3]. Later, this technology was used to induce resistance to a large number of viruses of different taxx onomic groups for a wide range of cultivated crops [4⎯6]. Despite the fact that this method of inducing resistance to viruses is one of the most commonly used, its mechanism is not completely clear and seems to be different for different viruses [7]. According to some data, the mechanism of resistance is based on the interference of the transgenic CP and wildtype CP [3]; however, other studies support the hypothesis about the …