Three-dimensional eukaryotic genomic organization is strongly correlated with codon usage expression and function

Strong association between genomic 3D structure and CRISPR cleavage efficiency

CRISPR is a gene editing technology which enables precise in-vivo genome editing; but its potential is hampered by its relatively low specificity and sensitivity. Improving CRISPR's on-target and off-target effects requires a better understanding of its mechanism and determinants. Here we demonstrate, for the first time, the chromosomal 3D spatial structure's association with CRISPR's cleavage efficiency, and its predictive capabilities. We used high-resolution Hi-C data to estimate the 3D distance between different regions in the human genome and utilized these spatial properties to generate 3D-based features, characterizing each region's density. We evaluated these features based on empirical, in-vivo CRISPR efficiency data and compared them to 425 features used in state-of-the-art models. The 3D features ranked in the top 13% of the features, and significantly improved the predictive power of LASSO and xgboost models trained with these features. The features indicated that sites with lower spatial density demonstrated higher efficiency. Understanding how CRISPR is affected by the 3D DNA structure provides insight into CRISPR's mechanism in general and improves our ability to correctly predict CRISPR's cleavage as well as design sgRNAs for therapeutic and scientific use.

Determinants of associations between codon and amino acid usage patterns of microbial communities and the environment inferred based on a cross-biome metagenomic analysis

Codon and amino acid usage were associated with almost every aspect of microbial life. However, how the environment may impact the codon and amino acid choice of microbial communities at the habitat level is not clearly understood. Therefore, in this study, we analyzed codon and amino acid usage patterns of a large number of environmental samples collected from diverse ecological niches. Our results suggested that samples derived from similar environmental niches, in general, show overall similar codon and amino acid distribution as compared to samples from other habitats. To substantiate the relative impact of the environment, we considered several factors, such as their similarity in GC content, or in functional or taxonomic abundance. Our analysis demonstrated that none of these factors can fully explain the trends that we observed at the codon or amino acid level implying a direct environmental influence on them. Further, our analysis demonstrated different levels of selection on codon bias in different microbial communities with the highest bias in host-associated environments such as the digestive system or oral samples and the lowest level of selection in soil and water samples. Considering a large number of metagenomic samples here we showed that microorganisms collected from similar environmental backgrounds exhibit similar patterns of codon and amino acid usage irrespective of the location or time from where the samples were collected. Thus our study suggested a direct impact of the environment on codon and amino usage of microorganisms that cannot be explained considering the influence of other factors.

Correlated Evolution of Large DNA Fragments in the 3D Genome of Arabidopsis thaliana

In eukaryotes, the three-dimensional (3D) conformation of the genome is far from random, and this nonrandom chromatin organization is strongly correlated with gene expression and protein function, which are two critical determinants of the selective constraints and evolutionary rates of genes. However, whether genes and other elements that are located close to each other in the 3D genome evolve in a coordinated way has not been investigated in any organism. To address this question, we constructed chromatin interaction networks (CINs) in Arabidopsis thaliana based on high-throughput chromosome conformation capture data and demonstrated that adjacent large DNA fragments in the CIN indeed exhibit more similar levels of polymorphism and evolutionary rates than random fragment pairs. Using simulations that account for the linear distance between fragments, we proved that the 3D chromosomal organization plays a role in the observed correlated evolution. Spatially interacting fragments also exhibit more similar mutation rates and functional constraints in both coding and noncoding regions than the random expectations, indicating that the correlated evolution between 3D neighbors is a result of combined evolutionary forces. A collection of 39 genomic and epigenomic features can explain much of the variance in genetic diversity and evolutionary rates across the genome. Moreover, features that have a greater effect on the evolution of regional sequences tend to show higher similarity between neighboring fragments in the CIN, suggesting a pivotal role of epigenetic modifications and chromatin organization in determining the correlated evolution of large DNA fragments in the 3D genome.

Computational discovery and modeling of novel gene expression rules encoded in the mRNA

The transcript is populated with numerous overlapping codes that regulate all steps of gene expression. Deciphering these codes is very challenging due to the large number of variables involved, the non-modular nature of the codes, biases and limitations in current experimental approaches, our limited knowledge in gene expression regulation across the tree of life, and other factors. In recent years, it has been shown that computational modeling and algorithms can significantly accelerate the discovery of novel gene expression codes. Here, we briefly summarize the latest developments and different approaches in the field.

Widespread non-modular overlapping codes in the coding regions

Messenger RNAs (mRNAs) consist of a coding region (open reading frame (ORF)) and two untranslated regions (UTRs), 5'UTR and 3'UTR. Ribosomes travel along the coding region, translating nucleotide triplets (called codons) to a chain of amino acids. The coding region was long believed to mainly encode the amino acid content of proteins, whereas regulatory signals reside in the UTRs and in other genomic regions. However, in recent years we have learned that the ORF is expansively populated with various regulatory signals, or codes, which are related to all gene expression steps and additional intracellular aspects. In this paper, we review the current knowledge related to overlapping codes inside the coding regions, such as the influence of synonymous codon usage on translation speed (and, in turn, the effect of translation speed on protein folding), ribosomal frameshifting, mRNA stability, methylation, splicing, transcription and more. All these codes come together and overlap in the ORF sequence, ensuring production of the right protein at the right time.

Deciphering Hi-C: from 3D genome to function

Hi-C is a commonly used technology in 3D genomics which can depict global chromatin interactions across eukaryotic genome. Integrating with different datasets, it can also be applied to studying various biological questions, such as nuclear organization, gene transcription regulation, spatiotemporal development, genome assembly, and cancer genomics. During the last decade, the development and application of Hi-C have dramatically changed the view of genome architecture, chromatin conformation, and gene interaction. So far, Hi-C-related studies remain vivacious and controversial; thus, a unified standard of library construction and bioinformatics analysis are urgently needed. In this review, we have summarized its history, development, methodologies, advances, applications, shortages, and future perspectives. We discuss a few limitations of the current Hi-C technologies and future directions for improvement and highlight how Hi-C can bridge 3D structure to gene function. This review will be helpful for scientists who want to engage in the 3D genomics field; it also shows some future tracks.

Tracking the evolution of 3D gene organization demonstrates its connection to phenotypic divergence

It has recently been shown that the organization of genes in eukaryotic genomes, and specifically in 3D, is strongly related to gene expression and function and partially conserved between organisms. However, previous studies of 3D genomic organization analyzed each organism independently from others. Here, we propose an approach for unified inter-organismal analysis of gene organization based on a network representation of Hi-C data. We define and detect four classes of spatially co-evolving orthologous modules (SCOMs), i.e. gene families that co-evolve in their 3D organization, based on patterns of divergence and conservation of distances. We demonstrate our methodology on Hi-C data from Saccharomyces cerevisiae and Schizosaccharomyces pombe, and identify, among others, modules relating to RNA splicing machinery and chromatin silencing by small RNA which are central to S. pombe's lifestyle. Our results emphasize the importance of 3D genomic organization in eukaryotes and suggest that the evolutionary mechanisms that shape gene organization affect the organism fitness and phenotypes. The proposed algorithms can be utilized in future studies of genome evolution and comparative analysis of spatial genomic organization in different tissues, conditions and single cells.

Rice black-streaked dwarf virus Genome in China: Diversification, Phylogeny, and Selection

Rice black-streaked dwarf virus (RBSDV), a Fijivirus, causes maize rough dwarf disease and rice black-streaked dwarf disease in the summer maize-growing regions of the Yellow and Huai rivers, respectively, in China. Nevertheless, the diversification and selection of the entire genome from S1 to S10 have not been illuminated. Molecular variation, evolution, conserved regions, and other genomic properties were analyzed in 21 RBSDV isolates from maize (Zea mays L.) and rice (Oryza sativa) hosts sampled from nine geographic locations in China. Low codon adaptation index values ranging from 0.1878 to 0.2918 indicated a low degree of codon-usage bias and low potential expression for all 13 RBSDV open reading frames (ORFs). ORF9-2 showed a stronger effect of codon usage bias than did other ORFs, as the majority of points for this ORF lay close to the standard curve in the Nc plot (the effective number of codons [Nc] versus the frequency of G+C at synonymous third-base positions [GC3]). A 9-bp deletion mutation was detected in the RBSDV genome in the 3' UTR of S8. Nucleotide diversity analysis indicated that the structural proteins of RBSDV, such as S2 and S4, were all more conserved than nonstructural proteins such as S9. Nucleotide diversity (π) was highest among S9 sequences (0.0656), and was significantly higher than among S4 sequences (0.0225, P < 0.01). The number of conserved regions among the 10 segments varied substantially. The highest number of conserved regions (5) was found in S5, whereas no conserved regions were identified in S9. Nucleotide diversity and the number of conserved regions were independent of the lengths of segments. Nucleotide diversity was also not correlated with the number of conserved regions in segments. Ten recombination events in 21 isolates were found in seven segments with breakpoint positions in UTRs, intergenic spacer regions, and gene coding regions. The number of recombination events was also independent of the lengths of segments. RBSDV isolates from China could be phylogenetically classified into two groups using either 10 segment sequences or the concatenated sequence of S1 through S10, regardless of host or geographical location. The phylogenetic tree generated from pairwise nucleotide identities of individual RBSDV segments such as S9 and S3, with nucleotide identity values of 93.74% and 95.86%, respectively, is similar to the tree constructed from the concatenated sequences of the entire RBSDV genome. The 13 RBSDV ORFs were under negative and purifying selection (Ka/Ks < 1). ORF5-2 was under the greatest selection pressure; however, ORF2, which encodes the core protein of RBSDV, was under the lowest selection pressure.

Extending partial haplotypes to full genome haplotypes using chromosome conformation capture data

MOTIVATION

Complex interactions among alleles often drive differences in inherited properties including disease predisposition. Isolating the effects of these interactions requires phasing information that is difficult to measure or infer. Furthermore, prevalent sequencing technologies used in the essential first step of determining a haplotype limit the range of that step to the span of reads, namely hundreds of bases. With the advent of pseudo-long read technologies, observable partial haplotypes can span several orders of magnitude more. Yet, measuring whole-genome-single-individual haplotypes remains a challenge. A different view of whole genome measurement addresses the 3D structure of the genome-with great development of Hi-C techniques in recent years. A shortcoming of current Hi-C, however, is the difficulty in inferring information that is specific to each of a pair of homologous chromosomes.

RESULTS

In this work, we develop a robust algorithmic framework that takes two measurement derived datasets: raw Hi-C and partial short-range haplotypes, and constructs the full-genome haplotype as well as phased diploid Hi-C maps. By analyzing both data sets together we thus bridge important gaps in both technologies-from short to long haplotypes and from un-phased to phased Hi-C. We demonstrate that our method can recover ground truth haplotypes with high accuracy, using measured biological data as well as simulated data. We analyze the impact of noise, Hi-C sequencing depth and measured haplotype lengths on performance. Finally, we use the inferred 3D structure of a human genome to point at transcription factor targets nuclear co-localization.

AVAILABILITY AND IMPLEMENTATION

The implementation available at https://github.com/YakhiniGroup/SpectraPh

CONTACT

zohar.yakhini@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Detecting the Orbital Angular Momentum of Electro-Magnetic Waves Using Virtual Rotational Antenna

Orbital Angular Momentum (OAM) is a typical spatial mode of an Electro-Magnetic (EM) wave. Correctly detecting the OAM mode is fundamental and of foremost importance when applying the phenomenon to wireless transmission in free space. It is found that when rotating an OAM wave, a rotational Doppler shift that is proportional to the rotation speed and the OAM mode number can be observed. This property can be used for OAM detection, i.e., different OAM modes are identified by measuring the corresponding rotational Doppler frequency shifts. In previous work, this method was implemented by mechanically rotating the OAM wave, resulting in a small frequency shift. Since the high-speed mechanical rotation is hard to manufacture in a real system, it brings limitations to the bandwidth for each OAM wave. In this paper, we report on an OAM mode detection method based on digitally rotating a virtual antenna. The transmitter and receiver are physically fixed, but the Virtual Rotational Antenna (VRA) is obtained by interpolating the signals received from transverse-mounted receiving antennas. A large rotational Doppler shift occurs as a consequence of using digital processing, resulting in more capability for wideband wireless data transmission with the larger shifted frequency.

A pathway-centric view of spatial proximity in the 3D nucleome across cell lines

In various contexts, spatially proximal genes have been shown to be functionally related. However, the extent to which spatial proximity of genes in a pathway contributes to the pathway's context-specific activity is not known. Leveraging Hi-C data in six human cell-lines, we show that spatial proximity of genes in a pathway is highly correlated with the pathway's context-specific expression and function. Furthermore, spatial proximity of pathway genes correlates with interactions of their protein products, and the specific pathway genes that are proximal to one another tend to occupy higher levels in the regulatory hierarchy. In addition to intra-pathway proximity, related pathways are spatially proximal to one another and housekeeping-genes tend to be proximal to several other pathways suggesting their coordinating role. Substantially extending previous works, our study reveals a pathway-centric organization of 3D-nucleome, whereby, functionally related interacting driver genes tend to be in spatial-proximity in a context-specific manner.

Multiplexing Genetic and Nucleosome Positioning Codes: A Computational Approach

Eukaryotic DNA is strongly bent inside fundamental packaging units: the nucleosomes. It is known that their positions are strongly influenced by the mechanical properties of the underlying DNA sequence. Here we discuss the possibility that these mechanical properties and the concomitant nucleosome positions are not just a side product of the given DNA sequence, e.g. that of the genes, but that a mechanical evolution of DNA molecules might have taken place. We first demonstrate the possibility of multiplexing classical and mechanical genetic information using a computational nucleosome model. In a second step we give evidence for genome-wide multiplexing in Saccharomyces cerevisiae and Schizosacharomyces pombe. This suggests that the exact positions of nucleosomes play crucial roles in chromatin function.

Molecular Genetic Analysis and Evolution of Segment 7 in Rice Black-Streaked Dwarf Virus in China

Rice black-streaked dwarf virus (RBSDV) causes maize rough dwarf disease or rice black-streaked dwarf disease and can lead to severe yield losses in maize and rice. To analyse RBSDV evolution, codon usage bias and genetic structure were investigated in 111 maize and rice RBSDV isolates from eight geographic locations in 2013 and 2014. The linear dsRNA S7 is A+U rich, with overall codon usage biased toward codons ending with A (A3s, S7-1: 32.64%, S7-2: 29.95%) or U (U3s, S7-1: 44.18%, S7-2: 46.06%). Effective number of codons (Nc) values of 45.63 in S7-1 (the first open reading frame of S7) and 39.96 in S7-2 (the second open reading frame of S7) indicate low degrees of RBSDV-S7 codon usage bias, likely driven by mutational bias regardless of year, host, or geographical origin. Twelve optimal codons were detected in S7. The nucleotide diversity (π) of S7 sequences in 2013 isolates (0.0307) was significantly higher than in 2014 isolates (0.0244, P = 0.0226). The nucleotide diversity (π) of S7 sequences in isolates from Jinan (0.0391) was higher than that from the other seven locations (P < 0.01). Only one S7 recombinant was detected in Baoding. RBSDV isolates could be phylogenetically classified into two groups according to S7 sequences, and further classified into two subgroups. S7-1 and S7-2 were under negative and purifying selection, with respective Ka/Ks ratios of 0.0179 and 0.0537. These RBSDV populations were expanding (P < 0.01) as indicated by negative values for Tajima's D, Fu and Li's D, and Fu and Li's F. Genetic differentiation was detected in six RBSDV subpopulations (P < 0.05). Absolute Fst (0.0790) and Nm (65.12) between 2013 and 2014, absolute Fst (0.1720) and Nm (38.49) between maize and rice, and absolute Fst values of 0.0085-0.3069 and Nm values of 0.56-29.61 among these eight geographic locations revealed frequent gene flow between subpopulations. Gene flow between 2013 and 2014 was the most frequent.

GC3-biased gene domains in mammalian genomes

Motivation: Synonymous codon usage bias has been shown to be correlated with many genomic features among different organisms. However, the biological significance of codon bias with respect to gene function and genome organization remains unclear.

Results: Guanine and cytosine content at the third codon position (GC3) could be used as a good indicator of codon bias. Here, we used relative GC3 bias values to compare the strength of GC3 bias of genes in human and mouse. We reported, for the first time, that GC3-rich and GC3-poor gene products might have distinct sub-cellular spatial distributions. Moreover, we extended the view of genomic gene domains and identified conserved GC3 biased gene domains along chromosomes. Our results indicated that similar GC3 biased genes might be co-translated in specific spatial regions to share local translational machineries, and that GC3 could be involved in the organization of genome architecture.

Availability and implementation: Source code is available upon request from the authors.

Contact:zhaozh@nic.bmi.ac.cn or zany1983@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Improving 3D Genome Reconstructions Using Orthologous and Functional Constraints

The study of the 3D architecture of chromosomes has been advancing rapidly in recent years. While a number of methods for 3D reconstruction of genomic models based on Hi-C data were proposed, most of the analyses in the field have been performed on different 3D representation forms (such as graphs). Here, we reproduce most of the previous results on the 3D genomic organization of the eukaryote Saccharomyces cerevisiae using analysis of 3D reconstructions. We show that many of these results can be reproduced in sparse reconstructions, generated from a small fraction of the experimental data (5% of the data), and study the properties of such models. Finally, we propose for the first time a novel approach for improving the accuracy of 3D reconstructions by introducing additional predicted physical interactions to the model, based on orthologous interactions in an evolutionary-related organism and based on predicted functional interactions between genes. We demonstrate that this approach indeed leads to the reconstruction of improved models.

Understanding the importance of genome architecture, the arrangement of genes within the genome and how this organization evolved has been intensively studied in recent years. Despite rapid progress in the field, accurate 3D modeling of genome organization remains a challenge. While a number of methods for 3D reconstruction of genomic models based on genome-wide experimental data were proposed, most of the analyses in the field have been performed on different 3D representation forms (such as graphs). Here, we reproduce most of the previous results on the 3D genome organization of the eukaryote Saccharomyces cerevisiae using analysis of 3D reconstructions. We show that many of these results can be reproduced in sparse reconstructions, generated from a small fraction of the experimental data (5% of the data), and study the properties of such models. Finally, we propose for the first time a novel approach for improving the accuracy of 3D reconstructions by introducing additional predicted physical interactions to the model, based on orthologous interactions in a different organism and based on predicted functional interactions between genes. Our proposed approach can facilitate future studies of 3D genome organization via improved models.