High-resolution crossover mapping reveals similarities and differences of male and female recombination in maize.

Meiotic crossovers (COs) are not uniformly distributed across the genome. Factors affecting this phenomenon are not well understood. Although many species exhibit large differences in CO numbers between sexes, sex-specific aspects of CO landscape are particularly poorly elucidated. Here, we conduct high-resolution CO mapping in maize. Our results show that CO numbers as well as their overall distribution are similar in male and female meioses. There are, nevertheless, dissimilarities at local scale. Male and female COs differ in their locations relative to transcription start sites in gene promoters and chromatin marks, including nucleosome occupancy and tri-methylation of lysine 4 of histone H3 (H3K4me3). Our data suggest that sex-specific factors not only affect male-female CO number disparities but also cause fine differences in CO positions. Differences between male and female CO landscapes indicate that recombination has distinct implications for population structure and gene evolution in male and in female meioses.

Correction to: The transcriptome landscape of early maize meiosis.

CORRECTION: Following publication of the original article [1], the authors reported that the number of genes overlaying the bar graph in Fig. 3A were incorrectly counted and inserted (i.e. including a title tile, and in inverse order). The corrected numbers are below and match with the listed genes supplied in Additional File: Table S2.

Genomic features shaping the landscape of meiotic double-strand-break hotspots in maize.

Meiotic recombination is the most important source of genetic variation in higher eukaryotes. It is initiated by formation of double-strand breaks (DSBs) in chromosomal DNA in early meiotic prophase. The DSBs are subsequently repaired, resulting in crossovers (COs) and noncrossovers (NCOs). Recombination events are not distributed evenly along chromosomes but cluster at recombination hotspots. How specific sites become hotspots is poorly understood. Studies in yeast and mammals linked initiation of meiotic recombination to active chromatin features present upstream from genes, such as absence of nucleosomes and presence of trimethylation of lysine 4 in histone H3 (H3K4me3). Core recombination components are conserved among eukaryotes, but it is unclear whether this conservation results in universal characteristics of recombination landscapes shared by a wide range of species. To address this question, we mapped meiotic DSBs in maize, a higher eukaryote with a large genome that is rich in repetitive DNA. We found DSBs in maize to be frequent in all chromosome regions, including sites lacking COs, such as centromeres and pericentromeric regions. Furthermore, most DSBs are formed in repetitive DNA, predominantly Gypsy retrotransposons, and only one-quarter of DSB hotspots are near genes. Genic and nongenic hotspots differ in several characteristics, and only genic DSBs contribute to crossover formation. Maize hotspots overlap regions of low nucleosome occupancy but show only limited association with H3K4me3 sites. Overall, maize DSB hotspots exhibit distribution patterns and characteristics not reported previously in other species. Understanding recombination patterns in maize will shed light on mechanisms affecting dynamics of the plant genome.

Gene Evolutionary Trajectories and GC Patterns Driven by Recombination in Zea mays.

Recombination occurring during meiosis is critical for creating genetic variation and plays an essential role in plant evolution. In addition to creating novel gene combinations, recombination can affect genome structure through altering GC patterns. In maize (Zea mays) and other grasses, another intriguing GC pattern exists. Maize genes show a bimodal GC content distribution that has been attributed to nucleotide bias in the third, or wobble, position of the codon. Recombination may be an underlying driving force given that recombination sites are often associated with high GC content. Here we explore the relationship between recombination and genomic GC patterns by comparing GC gene content at each of the three codon positions (GC1, GC2, and GC3, collectively termed GCx) to instances of a variable GC-rich motif that underlies double strand break (DSB) hotspots and to meiocyte-specific gene expression. Surprisingly, GCx bimodality in maize cannot be fully explained by the codon wobble hypothesis. High GCx genes show a strong overlap with the DSB hotspot motif, possibly providing a mechanism for the high evolutionary rates seen in these genes. On the other hand, genes that are turned on in meiosis (early prophase I) are biased against both high GCx genes and genes with the DSB hotspot motif, possibly allowing important meiotic genes to avoid DSBs. Our data suggests a strong link between the GC-rich motif underlying DSB hotspots and high GCx genes.

Fibrosis Progression in Patients With Chronic Hepatitis C Virus Infection.

BACKGROUND: Fibrosis progression varies markedly in hepatitis C virus (HCV)-infected individuals. We investigated factors that influence fibrosis progression in chronic HCV infection. METHODS: HCV-infected patients who underwent at least 2 liver biopsies were included in this study. Associations between fibrosis progression and epidemiologic, virologic, and disease-associated factors were analyzed using logistic regression and multistate Markov modeling. RESULTS: We analyzed 936 biopsy specimens obtained from 378 individuals. Mean age (±SD) at first biopsy was 48.3 ± 9.3 years, 59.3% of patients were male, 59.9% were white, and 86.7% were infected with HCV genotype 1. Fibrosis progression and cirrhosis occurred in 57.4% and 5.8%, respectively. Fibrosis progression between the first and last biopsies was associated with lower fibrosis in the first biopsy specimen (P < .001) and with the occurrence of at least 1 flare in the alanine aminotransferase (ALT) level (>200 U/L; P = .007). We found the highest fibrosis progression rate between stages 0 and 1 and the lowest between stages 2 and 3. Increased necroinflammation and higher ALT level were associated with faster progression. HCV genotype 3-infected patients were more likely to progress to cirrhosis (P < .001). CONCLUSIONS: Fibrosis progression in HCV is not linear but varies according to stage, with the highest progression in patients with the lowest fibrosis severity. Patients who experience flares in the ALT level are also more likely to progress.

The transcriptome landscape of early maize meiosis.

BACKGROUND: A major step in the higher plant life cycle is the decision to leave the mitotic cell cycle and begin the progression through the meiotic cell cycle that leads to the formation of gametes. The molecular mechanisms that regulate this transition and early meiosis remain largely unknown. To gain insight into gene expression features during the initiation of meiotic recombination, we profiled early prophase I meiocytes from maize (Zea mays) using capillary collection to isolate meiocytes, followed by RNA-seq. RESULTS: We detected ~2,000 genes as preferentially expressed during early meiotic prophase, most of them uncharacterized. Functional analysis uncovered the importance of several cellular processes in early meiosis. Processes significantly enriched in isolated meiocytes included proteolysis, protein targeting, chromatin modification and the regulation of redox homeostasis. The most significantly up-regulated processes in meiocytes were processes involved in carbohydrate metabolism. Consistent with this, many mitochondrial genes were up-regulated in meiocytes, including nuclear- and mitochondrial-encoded genes. The data were validated with real-time PCR and in situ hybridization and also used to generate a candidate maize homologue list of known meiotic genes from Arabidopsis. CONCLUSIONS: Taken together, we present a high-resolution analysis of the transcriptome landscape in early meiosis of an important crop plant, providing support for choosing genes for detailed characterization of recombination initiation and regulation of early meiosis. Our data also reveal an important connection between meiotic processes and altered/increased energy production.

Fast geometric consensus approach for protein model quality assessment.

Model quality assessment (MQA) is an integral part of protein structure prediction methods that typically generate multiple candidate models. The challenge lies in ranking and selecting the best models using a variety of physical, knowledge-based, and geometric consensus (GC)-based scoring functions. In particular, 3D-Jury and related GC methods assume that well-predicted (sub-)structures are more likely to occur frequently in a population of candidate models, compared to incorrectly folded fragments. While this approach is very successful in the context of diversified sets of models, identifying similar substructures is computationally expensive since all pairs of models need to be superimposed using MaxSub or related heuristics for structure-to-structure alignment. Here, we consider a fast alternative, in which structural similarity is assessed using 1D profiles, e.g., consisting of relative solvent accessibilities and secondary structures of equivalent amino acid residues in the respective models. We show that the new approach, dubbed 1D-Jury, allows to implicitly compare and rank N models in O(N) time, as opposed to quadratic complexity of 3D-Jury and related clustering-based methods. In addition, 1D-Jury avoids computationally expensive 3D superposition of pairs of models. At the same time, structural similarity scores based on 1D profiles are shown to correlate strongly with those obtained using MaxSub. In terms of the ability to select the best models as top candidates 1D-Jury performs on par with other GC methods. Other potential applications of the new approach, including fast clustering of large numbers of intermediate structures generated by folding simulations, are discussed as well.

Building and assessing atomic models of proteins from structural templates: learning and benchmarks.

One approach to predict a protein fold from a sequence (a target) is based on structures of related proteins that are used as templates. We present an algorithm that examines a set of candidates for templates, builds from each of the templates an atomically detailed model, and ranks the models. The algorithm performs a hierarchical selection of the best model using a diverse set of signals. After a quick and suboptimal screening of template candidates from the protein data bank, the current method fine-tunes the selection to a few models. More detailed signals test the compatibility of the sequence and the proposed structures, and are merged to give a global fitness measure using linear programming. This algorithm is a component of the prediction server LOOPP (http://www.loopp.org). Large-scale training and tests sets were designed and are presented. Recent results of the LOOPP server in CASP8 are discussed.

Support vector training of protein alignment models.

Sequence to structure alignment is an important step in homology modeling of protein structures. Incorporation of features such as secondary structure, solvent accessibility, or evolutionary information improve sequence to structure alignment accuracy, but conventional generative estimation techniques for alignment models impose independence assumptions that make these features difficult to include in a principled way. In this paper, we overcome this problem using a Support Vector Machine (SVM) method that provides a well-founded way of estimating complex alignment models with hundred of thousands of parameters. Furthermore, we show that the method can be trained using a variety of loss functions. In a rigorous empirical evaluation, the SVM algorithm outperforms the generative alignment method SSALN, a highly accurate generative alignment model that incorporates structural information. The alignment model learned by the SVM aligns 50% of the residues correctly and aligns over 70% of the residues within a shift of four positions.

A template-finding algorithm and a comprehensive benchmark for homology modeling of proteins.

The first step in homology modeling is to identify a template protein for the target sequence. The template structure is used in later phases of the calculation to construct an atomically detailed model for the target. We have built from the Protein Data Bank (PDB) a large-scale learning set that includes tens of millions of pair matches that can be either a true template or a false one. Discriminatory learning (learning from positive and negative examples) is used to train a decision tree. Each branch of the tree is a mathematical programming model. The decision tree is tested on an independent set from PDB entries and on the sequences of CASP7. It provides significant enrichment of true templates (between 50 and 100%) when compared to PSI-BLAST. The model is further verified by building atomically detailed structures for each of the tentative true templates with modeller. The probability that a true match does not yield an acceptable structural model (within 6 A RMSD from the native structure) decays linearly as a function of the TM structural-alignment score.

Enriching the sequence substitution matrix by structural information.

A fundamental step in homology modeling is the comparison of two protein sequences: a probe sequence with an unknown structure and function and a template sequence for which the structure and function are known. The detection of protein similarities relies on a substitution matrix that scores the proximity of the aligned amino acids. Sequence-to-sequence alignments use symmetric substitution matrices, whereas the threading protocols use asymmetric matrices, testing the fitness of the probe sequence into the structure of the template protein. We propose a linear combination of threading and sequence-alignment scoring function, to produce a single (mixed) scoring table. By fitting a single parameter (which is the relative contribution of the BLOSUM 50 matrix and the threading energy table of THOM2) we obtain a significant increase in prediction capacity in the twilight zone of homology modeling (detecting sequences with <25% sequence identity and with very similar structures). For a difficult test of 176 homologous pairs, with no signal of sequence similarity, the mixed model makes it possible to detect between 40 and 100% more protein pairs than the number of pairs that are detected by pure threading. Surprisingly, the linear combination of the two models is performing better than threading and than sequence alignment when the percentage of sequence identity is low. We finally suggest that further enrichment of substitution matrices, combing more structural descriptors such as exposed surface area, or secondary structure is expected to enhance the signal as well.

Clp protease complexes from photosynthetic and non-photosynthetic plastids and mitochondria of plants, their predicted three-dimensional structures, and functional implications.

Tetradecameric Clp protease core complexes in non-photosynthetic plastids of roots, flower petals, and in chloroplasts of leaves of Arabidopsis thaliana were purified based on native mass and isoelectric point and identified by mass spectrometry. The stoichiometry between the subunits was determined. The protease complex consisted of one to three copies of five different serine-type protease Clp proteins (ClpP1,3-6) and four non-proteolytic ClpR proteins (ClpR1-4). Three-dimensional homology modeling showed that the ClpP/R proteins fit well together in a tetradecameric complex and also indicated unique contributions for each protein. Lateral exit gates for proteolysis products are proposed. In addition, ClpS1,2, unique to land plants, tightly interacted with this core complex, with one copy of each per complex. The three-dimensional modeling show that they do fit well on the axial sites of the ClpPR cores. In contrast to plastids, plant mitochondria contained a single approximately 320-kDa homo-tetradecameric ClpP2 complex, without association of ClpR or ClpS proteins. It is surprising that the Clp core composition appears identical in all three plastid types, despite the remarkable differences in plastid proteome composition. This suggests that regulation of plastid proteolysis by the Clp machinery is not through differential regulation of ClpP/R/S gene expression, but rather through substrate recognition mechanisms and regulated interaction of chaperone-like molecules (ClpS1,2 and others) to the ClpP/R core.

Packing helices in proteins by global optimization of a potential energy function.

An efficient method has been developed for packing alpha-helices in proteins. It treats alpha-helices as rigid bodies and uses a simplified Lennard-Jones potential with Miyazawa-Jernigan contact-energy parameters to describe the interactions between the alpha-helical elements in this coarse-grained system. Global conformational searches to generate packing arrangements rapidly are carried out with a Monte Carlo-with-minimization type of approach. The results for 42 proteins show that the approach reproduces native-like folds of alpha-helical proteins as low-energy local minima of this highly simplified potential function.

Evolution of physics-based methodology for exploring the conformational energy landscape of proteins.

The evolution of our physics-based computational methods for determining protein conformation without the introduction of secondary-structure predictions, homology modeling, threading, or fragment coupling is described. Initial use of a hard-sphere potential captured much of the structural properties of polypeptide chains, and subsequent more refined force fields, together with efficient methods of global optimization provide indications that progress is being made toward an understanding of the interresidue interactions that underlie protein folding.

A method for optimizing potential-energy functions by a hierarchical design of the potential-energy landscape: application to the UNRES force field.

A method for optimizing potential-energy functions of proteins is proposed. The method assumes a hierarchical structure of the energy landscape, which means that the energy decreases as the number of native-like elements in a structure increases, being lowest for structures from the native family and highest for structures with no native-like element. A level of the hierarchy is defined as a family of structures with the same number of native-like elements (or degree of native likeness). Optimization of a potential-energy function is aimed at achieving such a hierarchical structure of the energy landscape by forcing appropriate free-energy gaps between hierarchy levels to place their energies in ascending order. This procedure is different from methods developed thus far, in which the energy gap and/or the Z score between the native structure and all non-native structures are maximized, regardless of the degree of native likeness of the non-native structures. The advantage of this approach lies in reducing the number of structures with decreasing energy, which should ensure the searchability of the potential. The method was tested on two proteins, PDB ID codes and, with an off-lattice united-residue force field. For, the search of the conformational space with the use of the conformational space annealing method and the newly optimized potential-energy function found the native structure very quickly, as opposed to the potential-energy functions obtained by former optimization methods. After even incomplete optimization, the force field obtained by using located the native-like structures of two peptides, and betanova (a designed three-stranded beta-sheet peptide), as the lowest-energy conformations, whereas for the 46-residue N-terminal fragment of staphylococcal protein A, the native-like conformation was the second-lowest-energy conformation and had an energy 2 kcal/mol above that of the lowest-energy structure.