Chromosomal Clusters Research Articles

The genome of Eleocharis vivipara elucidates the genetics of C3-C4 photosynthetic plasticity and karyotype evolution in the Cyperaceae.

Eleocharis vivipara, an amphibious sedge in the Cyperaceae family, has several remarkable properties, most notably its alternate use of C3 photosynthesis underwater and C4 photosynthesis on land. However, the absence of genomic data has hindered its utility for evolutionary and genetic research. Here, we present a high-quality genome for E. vivipara, representing the first chromosome-level genome for the Eleocharis genus, with an approximate size of 965.22 Mb mainly distributed across 10 chromosomes. Its Hi-C pattern, chromosome clustering results, and one-to-one genome synteny across two subgroups indicates a tetraploid structure with chromosome count 2n = 4x = 20. Phylogenetic analysis suggests that E. vivipara diverged from Cyperus esculentus approximately 32.96 million years ago (Mya), and underwent a whole-genome duplication (WGD) about 3.5 Mya. Numerous fusion and fission events were identified between the chromosomes of E. vivipara and its close relatives. We demonstrate that E. vivipara has holocentromeres, a chromosomal feature which can maintain the stability of such chromosomal rearrangements. Experimental transplantation and cross-section studies showed its terrestrial culms developed C4 Kranz anatomy with increased number of chloroplasts in the bundle sheath (BS) cells. Gene expression and weighted gene co-expression network analysis (WGCNA) showed overall elevated expression of core genes associated with the C4 pathway, and significant enrichment of genes related to modified culm anatomy and photosynthesis efficiency. We found evidence of mixed nicotinamide adenine dinucleotide - malic enzyme and phosphoenolpyruvate carboxykinase type C4 photosynthesis in E. vivipara, and hypothesize that the evolution of C4 photosynthesis predates the WGD event. The mixed type is dominated by subgenome A and supplemented by subgenome B. Collectively, our findings not only shed light on the evolution of E. vivipara and karyotype within the Cyperaceae family, but also provide valuable insights into the transition between C3 and C4 photosynthesis, offering promising avenues for crop improvement and breeding.

A liquid-like coat mediates chromosome clustering during mitotic exit

The individualization of chromosomes during early mitosis and their clustering upon exit from cell division are two key transitions that ensure efficient segregation of eukaryotic chromosomes. Both processes are regulated by the surfactant-like protein Ki-67, but how Ki-67 achieves these diametric functions has remained unknown. Here, we report that Ki-67 radically switches from a chromosome repellent to a chromosome attractant during anaphase in human cells. We show that Ki-67 dephosphorylation during mitotic exit and the simultaneous exposure of a conserved basic patch induce the RNA-dependent formation of a liquid-like condensed phase on the chromosome surface. Experiments and coarse-grained simulations support a model in which the coalescence of chromosome surfaces, driven by co-condensation of Ki-67 and RNA, promotes clustering of chromosomes. Our study reveals how the switch of Ki-67 from a surfactant to a liquid-like condensed phase can generate mechanical forces during genome segregation that are required for re-establishing nuclear-cytoplasmic compartmentalization after mitosis.

Adaptive Segmentation of DAPI-stained, C-banded, Aggregated and Overlapping Chromosomes.

Existing algorithms for automated segmentation of chromosomes and centromeres do not work well for condensed, C-banded and DAPI-stained chromosomes and centromeres. Overlapping and aggregation, which frequently occur in metaphase spreads, introduce additional challenges to the counting of chromosomes and centromeres in the Dicentrics Chromosome Assay (DCA). In this paper, we introduce adaptive algorithms, for segmentation of difficult metaphase spreads that include overlapping and aggregated chromosomes. In order to enhance and segment chromosomes, two optimizations are done: (1) the best algorithm among several options is automatically chosen based on predefined figures of merit, (2) the algorithm is automatically optimized with a binary search to modify its parameters to achieve predefined thresholds. These algorithms are designed to separate mildly or moderately aggregated chromosomal clusters. The clusters are segmented by skeleton junctions, reduction of the overall object thickness, and the watershed algorithm. The chromosomes are characterized by rules we establish, using minimal assumptions. Centromeres are detected by detecting bright spots on the surface of the chromosomes, and then using cluster analysis and shape and intensity profiles to identify them as centromeres. High sensitivity and specificity for chromosome and centromere detection were achieved.

PP2A-B55 phosphatase counteracts Ki-67-dependent chromosome individualization during mitosis

Cell cycle progression is regulated by the orderly balance between kinase and phosphatase activities. PP2A phosphatase holoenzymes containing the B55 family of regulatory B subunits function as major CDK1-counteracting phosphatases during mitotic exit in mammals. However, the identification of the specific mitotic roles of these PP2A-B55 complexes has been hindered by the existence of multiple B55 isoforms. Here, through the generation of loss-of-function genetic mouse models for the two ubiquitous B55 isoforms (B55α and B55δ), we report that PP2A-B55α and PP2A-B55δ complexes display overlapping roles in controlling the dynamics of proper chromosome individualization and clustering during mitosis. In the absence of PP2A-B55 activity, mitotic cells display increased chromosome individualization in the presence of enhanced phosphorylation and perichromosomal loading of Ki-67. These data provide experimental evidence for a regulatory mechanism by which the balance between kinase and PP2A-B55 phosphatase activity controls the Ki-67-mediated spatial organization of the mass of chromosomes during mitosis.

Sinorhizobium meliloti GR4 Produces Chromosomal- and pSymA-Encoded Type IVc Pili That Influence the Interaction with Alfalfa Plants.

Type IVc Pili (T4cP), also known as Tad or Flp pili, are long thin microbial filaments that are made up of small-sized pilins. These appendages serve different functions in bacteria, including attachment, biofilm formation, surface sensing, motility, and host colonization. Despite their relevant role in diverse microbial lifestyles, knowledge about T4cP in bacteria that establish symbiosis with legumes, collectively referred to as rhizobia, is still limited. Sinorhizobium meliloti contains two clusters of T4cP-related genes: flp-1 and flp-2, which are located on the chromosome and the pSymA megaplasmid, respectively. Bundle-forming pili associated with flp-1 are involved in the competitive nodulation of alfalfa plants, but the role of flp-2 remains elusive. In this work, we have performed a comprehensive bioinformatic analysis of T4cP genes in the highly competitive S. meliloti GR4 strain and investigated the role of its flp clusters in pilus biogenesis, motility, and in the interaction with alfalfa. Single and double flp-cluster mutants were constructed on the wild-type genetic background as well as in a flagellaless derivative strain. Our data demonstrate that both chromosomal and pSymA flp clusters are functional in pili biogenesis and contribute to surface translocation and nodule formation efficiency in GR4. In this strain, the presence of flp-1 in the absence of flp-2 reduces the competitiveness for nodule occupation.

Haplotype-resolved assembly of a tetraploid potato genome using long reads and low-depth offspring data

Potato is one of the world’s major staple crops, and like many important crop plants, it has a polyploid genome. Polyploid haplotype assembly poses a major computational challenge. We introduce a novel strategy for the assembly of polyploid genomes and present an assembly of the autotetraploid potato cultivar Altus. Our method uses low-depth sequencing data from an offspring population to achieve chromosomal clustering and haplotype phasing on the assembly graph. Our approach generates high-quality assemblies of individual chromosomes with haplotype-specific sequence resolution of whole chromosome arms and can be applied in common breeding scenarios where collections of offspring are available.

New algorithms for structure informed genome rearrangement

We define two new computational problems in the domain of perfect genome rearrangements, and propose three algorithms to solve them. The rearrangement scenarios modeled by the problems consider Reversal and Block Interchange operations, and a PQ-tree is utilized to guide the allowed operations and to compute their weights. In the first problem, ConstrainedTreeToStringDivergence\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathsf {Constrained \\ TreeToString \\ Divergence}$$\\end{document} (CTTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{CTTSD}{}$$\\end{document}), we define the basic structure-informed rearrangement measure. Here, we assume that the gene order members of the gene cluster from which the PQ-tree is constructed are permutations. The PQ-tree representing the gene cluster is ordered such that the series of gene IDs spelled by its leaves is equivalent to that of the reference gene order. Then, a structure-informed genome rearrangement distance is computed between the ordered PQ-tree and the target gene order. The second problem, TreeToStringDivergence\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathsf {TreeToString \\ Divergence}$$\\end{document} (TTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{TTSD}{}$$\\end{document}), generalizes CTTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{CTTSD}{}$$\\end{document}, where the gene order members are not necessarily permutations and the structure informed rearrangement measure is extended to also consider up to dS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_S$$\\end{document} and dT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_T$$\\end{document} gene insertion and deletion operations, respectively, when modelling the PQ-tree informed divergence process from the reference gene order to the target gene order. The first algorithm solves CTTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{CTTSD}{}$$\\end{document} in O(nγ2·(mp·1.381γ+mq))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n \\gamma ^2 \\cdot (m_p \\cdot 1.381^\\gamma + m_q))$$\\end{document} time and O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2)$$\\end{document} space, where γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document} is the maximum number of children of a node, n is the length of the string and the number of leaves in the tree, and mp\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m_p$$\\end{document} and mq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m_q$$\\end{document} are the number of P-nodes and Q-nodes in the tree, respectively. If one of the penalties of CTTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{CTTSD}$$\\end{document} is 0, then the algorithm runs in O(nmγ2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n m \\gamma ^2)$$\\end{document} time and O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2)$$\\end{document} space. The second algorithm solves TTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{TTSD}{}$$\\end{document} in O(n2γ2dT2dS2m2(mp·5γγ+mq))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2 \\gamma ^2 {d_T}^2 {d_S}^2\\,m^2 (m_p \\cdot 5^\\gamma \\gamma + m_q))$$\\end{document} time and O(dTdSm(mn+5γ))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(d_T d_S m (m n + 5^\\gamma ))$$\\end{document} space, where γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document} is the maximum number of children of a node, n is the length of the string, m is the number of leaves in the tree, mp\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m_p$$\\end{document} and mq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m_q$$\\end{document} are the number of P-nodes and Q-nodes in the tree, respectively, and allowing up to dT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_T$$\\end{document} deletions from the tree and up to dS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_S$$\\end{document} deletions from the string. The third algorithm is intended to reduce the space complexity of the second algorithm. It solves a variant of the problem (where one of the penalties of TTSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{TTSD}$$\\end{document} is 0) in O(nγ2dT2dS2m2(mp·4γγ2n(dT+dS+m+n)+mq))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n \\gamma ^2 {d_T}^2 {d_S}^2\\,m^2 (m_p \\cdot 4^{\\gamma }\\gamma ^2n(d_T+d_S+m+n) + m_q))$$\\end{document} time and O(γ2nm2dTdS(dT+dS+m+n))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\gamma ^2 n m^2 d_T d_S (d_T+d_S+m+n))$$\\end{document} space. The algorithm is implemented as a software tool, denoted MEM-Rearrange, and applied to the comparative and evolutionary analysis of 59 chromosomal gene clusters extracted from a dataset of 1487 prokaryotic genomes.

An automatic progressive chromosome segmentation approach using deep learning with traditional image processing.

The fully automatic chromosome analysis system plays an important role in the detection of genetic diseases, which in turn can reduce the diagnosis burden for cytogenetic experts. Chromosome segmentation is a critical step for such a system. However, due to the non-rigid structure of chromosomes, chromosomes may curve in any direction, and two or more chromosomes may touch or overlap to form unpredictable chromosome clusters in metaphase chromosome images, leading to automatic chromosome segmentation as a challenge. In this paper, we propose an automatic progressive segmentation approach to perform the entire metaphase chromosome image segmentation using deep learning with traditional image processing. It follows three stages. In the first stage, thresholding-based and geometric-based methods are employed to divide all chromosomes as single ones and chromosome clusters. To tackle the segmentation for unpredictable chromosome clusters, we first present a new chromosome cluster identification network named CCI-Net to classify all chromosome clusters into different types in the second stage, and then in the third stage, we combine traditional image processing with deep CNNs to accomplish chromosome instance segmentation from different types of clusters. Evaluation results on a clinical dataset of 1148 metaphase chromosome images show that the proposed automatic progressive segmentation method achieves 94.60% chromosome cluster identification accuracy and 99.15% instance segmentation accuracy. The experimental results exhibit that our proposed approach can effectively identify chromosome clusters and successfully perform fully automatic chromosome segmentation.

DNA Methylation Identifies Epigenetic Subtypes of Triple-Negative Breast Cancers With Distinct Clinicopathologic and Molecular Features

Triple-negative breast cancers (TNBC) include diverse carcinomas with heterogeneous clinical behavior. DNA methylation is a useful tool in classifying a variety of cancers. In this study, we analyzed TNBC using DNA methylation profiling and compared the results to those of mutational analysis. DNA methylation profiling (Infinium MethylationEPIC array, Illumina) and 50-gene panel–targeted DNA sequencing were performed in 44 treatment-naïve TNBC. We identified 3 distinct DNA methylation clusters with specific clinicopathologic and molecular features. Cluster 1 (phosphoinositide 3-kinase/protein kinase B–enriched cluster; n = 9) patients were significantly older (mean age, 71 years; P = .008) with tumors that were more likely to exhibit apocrine differentiation (78%; P < .001), a lower grade (44% were grade 2), a lower proliferation index (median Ki-67, 15%; P = .002), and lower tumor-infiltrating lymphocyte fractions (median, 15%; P = .0142). Tumors carried recurrent PIK3CA and AKT1 mutations and a higher percentage of low HER-2 expression (89%; P = .033). Cluster 3 (chromosomal instability cluster; n = 28) patients were significantly younger (median age, 57 years). Tumors were of higher grade (grade 3, 93%), had a higher proliferation index (median Ki-67, 75%), and were with a high fraction of tumor-infiltrating lymphocytes (median, 30%). Ninety-one percent of the germline BRCA1/2 mutation carriers were in cluster 3, and these tumors showed the highest level of copy number alterations. Cluster 2 represented cases with intermediate clinicopathologic characteristics and no specific molecular profile (no specific molecular profile cluster; n = 7). There were no differences in relation to stage, recurrence, and survival. In conclusion, DNA methylation profiling is a promising tool to classify patients with TNBC into biologically relevant groups, which may result in better disease characterization and reveal potential targets for emerging therapies.

Telomere-to-telomere haplotype-resolved reference genome reveals subgenome divergence and disease resistance in triploid Cavendish banana.

Banana is one of the most important crops of the world. Cavendish-type bananas, which have a monospecific Musa acuminata origin (AAA), account for around half of the global banana production, thereby are of great significance for human societies. However, until now, the high-quality haplotype-resolved reference genome was still undecoded for banana cultivars. Here, we reported the telomere-to-telomere (T2T) and haplotype-resolved reference genome of 'Baxijiao' (Cavendish) consisting of three haploid assemblies. The sizes of the three haploid assemblies were estimated to be 477.16Mb, 477.18Mb, and 469.57Mb, respectively. Although with monospecific origins, the three haploid assemblies showed great differences with low levels of sequence collinearity. Several large reciprocal translocations were identified among chromosomes 1, 4, and 7. An expansion of gene families that might affect fruit quality and aroma was detected, such as those belonging to sucrose/disaccharide/oligosaccharide catabolic processes, sucrose metabolic process, starch metabolic process, and aromatic compound biosynthetic process. Besides, an expansion of gene families related to anther and pollen development was observed, which could be associated with parthenocarpy and sterility of the Cavendish cultivar. Finally, much fewer resistance genes were identified in 'Baxijiao' than in M. acuminata, particularly in the gene clusters in chromosomes 3 and 10, providing potential targets to explore for molecular analysis of disease resistance in banana. This T2T haplotype-resolved reference genome will thus be a valuable genetic resource for biological studies, molecular breeding, and genetic improvement of banana.

Functional characterization of VirB/VirD4 and Icm/Dot type IV secretion systems from the plant-pathogenic bacterium Xanthomonas euvesicatoria.

Many Gram-negative plant- and animal-pathogenic bacteria employ type IV secretion (T4S) systems to transport proteins or DNA/protein complexes into eukaryotic or bacterial target cells. T4S systems have been divided into minimized and expanded T4S systems and resemble the VirB/VirD4 T4S system from the plant pathogen Agrobacterium tumefaciens and the Icm/Dot T4S system from the human pathogen Legionella pneumophila, respectively. The only known plant pathogen with both types of T4S systems is Xanthomonas euvesicatoria which is the causal agent of bacterial spot disease on pepper and tomato plants. In the present study, we show that virB/virD4 and icm/dot T4S genes are expressed and encode components of oligomeric complexes corresponding to known assemblies of VirB/VirD4 and Icm/Dot proteins. Both T4S systems are dispensable for the interaction of X. euvesicatoria with its host plants and do not seem to confer contact-dependent lysis of other bacteria, which was previously shown for the chromosomally encoded VirB/VirD4 T4S system from Xanthomonas axonopodis pv. citri. The corresponding chromosomal T4S gene cluster from X. euvesicatoria is incomplete, however, the second plasmid-localized vir gene cluster encodes a functional VirB/VirD4 T4S system which contributes to plasmid transfer. In agreement with this finding, we identified the predicted relaxase TraI as substrate of the T4S systems from X. euvesicatoria. TraI and additional candidate T4S substrates with homology to T4S effectors from X. axonopodis pv. citri interact with the T4S coupling protein VirD4. Interestingly, however, the predicted C-terminal VirD4-binding sites are not sufficient for T4S, suggesting the contribution of additional yet unknown mechanisms to the targeting of T4S substrates from X. euvesicatoria to both VirB/VirD4 and Icm/Dot T4S systems.

Chromosome Cluster Type Identification Using a Swin Transformer

The analysis of chromosome karyotypes is crucial for diagnosing genetic disorders such as Patau syndrome, Edward syndrome, and Down syndrome. Chromosome cluster type identification is a key step in the automated analysis of chromosome karyotypes. State-of-the-art chromosome cluster-type identification techniques are based on convolutional neural networks (CNNs) and fail to exploit the global context. To address this limitation of the state of the art, this paper proposes a transformer network, chromosome cluster transformer (CCT), that exploits a swin transformer backbone and successfully captures long-range dependencies in a chromosome image. Additionally, we find that the proposed CCT has a large number of model parameters, which makes it prone to overfitting on a (small) dataset of chromosome images. To alleviate the limited availability of training data, the proposed CCT also utilizes a transfer learning approach. Experiments demonstrate that the proposed CCT outperforms the state-of-the-art chromosome cluster type identification methods as well as the traditional vision transformer. Furthermore, to provide insights on the improved performance, we demonstrate the activation maps obtained using Gradient Attention Rollout.

Analysis of genetic variants in the exon 2 and 3 of autosomal DAZLA gene among infertile South Indian men

BackgroundBoule, DAZLA, and DAZ are members of the Deleted in Azoospermia family of genes, which play significant roles in gametogenesis and are important fertility factors in humans. In a few studies, deletion of the Y chromosomal DAZ cluster and single nucleotide polymorphisms in the DAZLA gene were reported to affect male fertility, although this is paradoxical because they were found in both control and fertile men. As a result, the goal of this study was to check if Y chromosomal DAZ cluster deletion and SNPs in the DAZLA gene on chromosome 3 of humans are associated to male infertility in the population. For computational analysis, different bioinformatics tools such as SIFT, PolyPhen2, Mutation Taster, FATHMM, and PROVEAN were used to analyse mutations.ResultsWithin the studied population, we found no association between DAZ deletion and the most prevalent DAZLA SNPs A260G (rs11710967) and A386G (rs1219183446). We also discovered two new deleterious genetic variations in exon 3 of the DAZLA gene, one nonsynonymous mutation that replaced Valine with Glutamate at the 66 codon position and the other a stop gain mutation at the 74 amino acid position. These genetic changes are found in the RRM domain of the DAZLA gene, which is confirmed by Motif scan analysis and results in a change in the DAZLA protein's secondary structure. The RRM domain is a highly conserved regulatory domain for mRNA transport and translation. Azoospermia and necrospermia infertility phenotypes were shown in infertile male samples with these genotypes.ConclusionWe can conclude that further investigation of the aforesaid new mutations in the DAZLA gene may be valuable in understanding their significance in male infertility in different populations due to the multifactorial nature of male infertility and arrays of gene expression required at every stage of spermatogenesis.

The courtship choreography of homologous chromosomes: timing and mechanisms of DSB-independent pairing.

Meiosis involves deep changes in the spatial organisation and interactions of chromosomes enabling the two primary functions of this process: increasing genetic diversity and reducing ploidy level. These two functions are ensured by crucial events such as homologous chromosomal pairing, synapsis, recombination and segregation. In most sexually reproducing eukaryotes, homologous chromosome pairing depends on a set of mechanisms, some of them associated with the repair of DNA double-strand breaks (DSBs) induced at the onset of prophase I, and others that operate before DSBs formation. In this article, we will review various strategies utilised by model organisms for DSB-independent pairing. Specifically, we will focus on mechanisms such as chromosome clustering, nuclear and chromosome movements, as well as the involvement of specific proteins, non-coding RNA, and DNA sequences.

Automated identification of single and clustered chromosomes for metaphase image analysis

BackgroundChromosome analysis is laborious and time-consuming. Automated methods can significantly increase the efficiency of chromosome analysis. For the automated analysis of chromosome images, single and clustered chromosomes must be identified. Herein, we propose a feature-based method for distinguishing between single chromosomes and clustered chromosome. MethodThe proposed method comprises three main steps. In the first step, chromosome objects are segmented from metaphase chromosome images in advance. In the second step, seven features are extracted from each segmented object, i.e., the normalized area, area/boundary ratio, side branch index, exhaustive thresholding index, normalized minimum width, minimum concave angle, and maximum boundary shift. Finally, the segmented objects are classified as a single chromosome or chromosome cluster using a combination of the seven features. ResultsIn total, 43,391 segmented objects, including 39,892 single chromosomes and 3,499 chromosome clusters, are used to evaluate the proposed method. The results show that the proposed method achieves an accuracy of 98.92% by combining the seven features using support vector machine. ConclusionsThe proposed method is highly effective in distinguishing between single and clustered chromosomes and can be used as a preprocessing procedure for automated chromosome image analysis.

Chromosome instability-associated prognostic signature and cluster investigation for cutaneous melanoma cases.

Chromosomal instability (CIN) is closely associated to the early detection of several clinical tumours. In this study, the authors first established a novel prognostic model of melanoma using the hub genes of CIN, based on the datasets of The cancer genome atlas-skin cutaneous melanoma (TCGA-SKCM) and GSE65904 cohorts. Based on the risk scores of our model, the disease-specific survival (DSS) prognosis was worse in the high-risk group. Combining risk score, stage, age, ulceration, and clark factors, a Nomogram was generated to predict 1, 3, 5-year survival rates, which indicated a good clinical validity. Our finding also showed a correlation between high/low risk and tumour infiltration levels of 'activated CD8 T cells' and 'effector memory CD8 T cells'. Moreover, the authors first performed a CIN-based tumour clustering analysis using TCGA-SKCM cases, and identified two melanoma clusters, which exhibit the distinct DSS prognosis and the tumour-infiltrating levels of CD8 T cells. Taken together, a promising CIN-related prognostic signature and clustering for melanoma cases were first established in our study.

Meiosis: Actin and microtubule networks drive chromosome clustering in oocytes

Actin and microtubule networks in human and porcine oocytes sequentially gather chromosomes in a cluster shortly after nuclear envelope breakdown to ensure their complete capture by the meiotic spindle.

Comparative genomic analysis provides insights into taxonomy and temperature adaption of Aeromonas salmonicida.

Aeromonas salmonicida has long been known as psychrophiles since it is mainly isolated from cold water fish, and recent reports have revealed the existence of mesophilic strains isolated from warm sources. However, the genetic differences between mesophilic and psychrophilic strains remain unclear due to few complete genomes of mesophilic strain are available. In this study, six A. salmonicida (2 mesophilic and 4 psychrophilic) were genome-sequenced, and comparative analyses of 25 A. salmonicida complete genomes were conducted. The ANI values and phylogenetic analysis revealed that 25 strains formed three independent clades, which were referred as typical psychrophilic, atypical psychrophilic and mesophilic groups. Comparative genomic analysis showed that two chromosomal gene clusters, related to lateral flagella and outer membrane proteins (A-layer and T2SS proteins), and insertion sequences (ISAs4, ISAs7 and ISAs29) were unique to the psychrophilic groups, while the complete MSH type IV pili were unique to the mesophilic group, all of which may be considered as lifestyle-related factors. The results of this study not only provide new insights into the classification, lifestyle adaption and pathogenic mechanism of different strains of A.salmonicida, but also contributes to the prevention and control of disease caused by psychrophilic and mesophilic A. salmonicida.

Two differentially stable rDNA loci coexist on the same chromosome and form a single nucleolus

The nucleolus is the most prominent membraneless compartment within the nucleus-dedicated to the metabolism of ribosomal RNA. Nucleoli are composed of hundreds of ribosomal DNA (rDNA) repeated genes that form large chromosomal clusters, whose high recombination rates can cause nucleolar dysfunction and promote genome instability. Intriguingly, the evolving architecture of eukaryotic genomes appears to have favored two strategic rDNA locations-where a single locus per chromosome is situated either near the centromere (CEN) or the telomere. Here, we deployed an innovative genome engineering approach to cut and paste to an ectopic chromosomal location-the ~1.5 mega-base rDNA locus in a single step using CRISPR technology. This "megablock" rDNA engineering was performed in a fused-karyotype strain of Saccharomyces cerevisiae. The strategic repositioning of this locus within the megachromosome allowed experimentally mimicking and monitoring the outcome of an rDNA migratory event, in which twin rDNA loci coexist on the same chromosomal arm. We showed that the twin-rDNA yeast readily adapts, exhibiting wild-type growth and maintaining rRNA homeostasis, and that the twin loci form a single nucleolus throughout the cell cycle. Unexpectedly, the size of each rDNA array appears to depend on its position relative to the CEN, in that the locus that is CEN-distal undergoes size reduction at a higher frequency compared to the CEN-proximal counterpart. Finally, we provided molecular evidence supporting a mechanism called paralogous cis-rDNA interference, which potentially explains why placing two identical repeated arrays on the same chromosome may negatively affect their function and structural stability.

Fundamentals and application in phytoremediation of an efficient arsenate reducing bacterium Pseudomonas putida ARS1

Arsenic is a ubiquitous environmental pollutant. Microbe-mediated arsenic bio-transformations significantly influence arsenic mobility and toxicity. Arsenic transformations by soil and aquatic organisms have been well documented, while little is known regarding effects due to endophytic bacteria. An endophyte Pseudomonas putida ARS1 was isolated from rice grown in arsenic contaminated soil. P. putida ARS1 shows high tolerance to arsenite (As(III)) and arsenate (As(V)), and exhibits efficient As(V) reduction and As(III) efflux activities. When exposed to 0.6 mg/L As(V), As(V) in the medium was completely converted to As(III) by P. putida ARS1 within 4 hr. Genome sequencing showed that P. putida ARS1 has two chromosomal arsenic resistance gene clusters (arsRCBH) that contribute to efficient As(V) reduction and As(III) efflux, and result in high resistance to arsenicals. Wolffia globosa is a strong arsenic accumulator with high potential for arsenic phytoremediation, which takes up As(III) more efficiently than As(V). Co-culture of P. putida ARS1 and W. globosa enhanced arsenic accumulation in W. globosa by 69%, and resulted in 91% removal of arsenic (at initial concentration of 0.6 mg/L As(V)) from water within 3 days. This study provides a promising strategy for in situ arsenic phytoremediation through the cooperation of plant and endophytic bacterium.