Genome stability from the perspective of telomere length.

Telomeres and their associated proteins protect the ends of chromosomes to maintain genome stability. Telomeres undergo progressive shortening with each cell division in mammalian somatic cells without telomerase, resulting in genome instability. When telomeres reach a critically short length or are recognized as a damage signal, cells enter a state of senescence, followed by cell cycle arrest, programmed cell death, or immortalization. This review provides an overview of recent advances in the intricate relationship between telomeres and genome instability. Alongside well-established mechanisms such as chromosomal fusion and telomere fusion, we will delve into the perspective on genome stability by examining the role of retrotransposons. Retrotransposons represent an emerging pathway to regulate genome stability through their interactions with telomeres.

Phosphocholine-induced energy source shift alleviates mitochondrial dysfunction in lung cells caused by geospecific PM2.5 components.

Fine particulate matter (PM2.5) is globally recognized for its adverse implications on human health. Yet, remain limited the individual contribution of particular PM2.5 components to its toxicity, especially considering regional disparities. Moreover, prevention solutions for PM2.5-associated health effects are scarce. In the present study, we comprehensively characterized and compared the primary PM2.5 constituents and their altered metabolites from two locations: Taiyuan and Guangzhou. Analysis of year-long PM2.5 samples revealed 84 major components, encompassing organic carbon, elemental carbon, ions, metals, and organic chemicals. PM2.5 from Taiyuan exhibited higher contamination, associated health risks, dithiothreitol activity, and cytotoxicities than Guangzhou's counterpart. Applying metabolomics, BEAS-2B lung cells exposed to PM2.5 from both cities were screened for significant alterations. A correlation analysis revealed the metabolites altered by PM2.5 and the critical toxic PM2.5 components in both regions. Among the PM2.5-down-regulated metabolites, phosphocholine emerged as a promising intervention for PM2.5 cytotoxicities. Its supplementation effectively attenuated PM2.5-induced energy metabolism disorder and cell death via activating fatty acid oxidation and inhibiting Phospho1 expression. The highlighted toxic chemicals displayed combined toxicities, potentially counteracted by phosphocholine. Our study offered a promising functional metabolite to alleviate PM2.5-induced cellular disorder and provided insights into the geo-based variability in toxic PM2.5 components.

Dissecting pleiotropic functions of the wheat Green Revolution gene Rht-B1b in plant morphogenesis and yield formation.

The utilization of reduced plant height genes Rht-B1b and Rht-D1b, encoding homeologous DELLA proteins, led to the wheat Green Revolution (GR). However, the specific functions of GR genes in yield determination and the underlying regulatory mechanisms remained unknown. Here, we validated that Rht-B1b, as a representative of GR genes, affects plant architecture and yield component traits. Upregulation of Rht-B1b reduced plant height, leaf size and grain weight, but increased tiller number, tiller angle, spike number per unit area, and grain number per spike. Dynamic investigations showed that Rht-B1b increased spike number by improving tillering initiation rather than outgrowth, and enhanced grain number by promoting floret fertility. Rht-B1b reduced plant height by reducing cell size in the internodes, and reduced grain size or weight by decreasing cell number in the pericarp. Transcriptome analyses uncovered that Rht-B1b regulates many homologs of previously reported key genes for given traits and several putative integrators for different traits. These findings specify the pleiotropic functions of Rht-B1b in improving yield and provide new insights into the regulatory mechanisms underlying plant morphogenesis and yield formation.

Fundamental limits in structured principal component analysis and how to reach them.

How do statistical dependencies in measurement noise influence high-dimensional inference? To answer this, we study the paradigmatic spiked matrix model of principal components analysis (PCA), where a rank-one matrix is corrupted by additive noise. We go beyond the usual independence assumption on the noise entries, by drawing the noise from a low-order polynomial orthogonal matrix ensemble. The resulting noise correlations make the setting relevant for applications but analytically challenging. We provide characterization of the Bayes optimal limits of inference in this model. If the spike is rotation invariant, we show that standard spectral PCA is optimal. However, for more general priors, both PCA and the existing approximate message-passing algorithm (AMP) fall short of achieving the information-theoretic limits, which we compute using the replica method from statistical physics. We thus propose an AMP, inspired by the theory of adaptive Thouless-Anderson-Palmer equations, which is empirically observed to saturate the conjectured theoretical limit. This AMP comes with a rigorous state evolution analysis tracking its performance. Although we focus on specific noise distributions, our methodology can be generalized to a wide class of trace matrix ensembles at the cost of more involved expressions. Finally, despite the seemingly strong assumption of rotation-invariant noise, our theory empirically predicts algorithmic performance on real data, pointing at strong universality properties.

OSCAA: A two-dimensional Gaussian mixture model for copy number variation association analysis.

Copy number variants (CNVs) are prevalent in the human genome and are found to have a profound effect on genomic organization and human diseases. Discovering disease-associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome-wide assessment of such variation. In this article, we developed One-Stage CNV-disease Association Analysis (OSCAA), a flexible algorithm to discover disease-associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the PCs from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV-disease association, especially for short CNVs or CNVs with weak signals. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.

Evolution and post-transcriptional regulation insights of m6A writers, erasers, and readers in plant epitranscriptome.

As a dynamic and reversible post-transcriptional marker, N6-methyladenosine (m6A) plays an important role in the regulation of biological functions, which are mediated by m6A pathway components including writers (MT-A70, FIP37, VIR and HAKAI family), erasers (ALKBH family) and readers (YTH family). There is an urgent need for a comprehensive analysis of m6A pathway components across species at evolutionary levels. In this study, we identified 4062 m6A pathway components from 154 plant species including green algae, utilizing large-scale phylogenetic to explore their origin and evolution. We discovered that the copy number of writers was conserved among different plant lineages, with notable expansions in the ALKBH and YTH families. Synteny network analysis revealed conserved genomic contexts and lineage-specific transpositions. Furthermore, we used Direct RNA Sequencing (DRS) to reveal the Poly(A) length (PAL) and m6A ratio profiles in six angiosperms species, with a particular focus on the m6A pathway components. The ECT1/2-Poeaece4 sub-branches (YTH family) with unique genomic contexts exhibited significantly higher expression level than genes of other ECT1/2 poeaece sub-branches (ECT1/2-Poeaece1-3), accompanied by lower m6A modification and PAL. Besides, conserved m6A sites distributed in CDS and 3'UTR were detected in the ECT1/2-Poaceae4, and the dual-luciferase assay further demonstrated that these conserved m6A sites in the 3'UTR negatively regulated the expression of Firefly luciferase (LUC) gene. Finally, we developed transcription factor regulatory networks for m6A pathway components, using yeast one-hybrid assay demonstrated that PheBPC1 could interact with the PheECT1/2-5 promoter. Overall, this study presents a comprehensive evolutionary and functional analysis of m6A pathway components and their modifications in plants, providing a valuable resource for future functional analysis in this field.

Carbon usage in yellow-fleshed Manihot esculenta storage roots shifts from starch biosynthesis to cell wall and raffinose biosynthesis via the myo-inositol pathway.

Cassava is a crucial staple crop for smallholder farmers in tropical Asia and Sub-Saharan Africa. Although high yield remains the top priority for farmers, the significance of nutritional values has increased in cassava breeding programs. A notable negative correlation between provitamin A and starch accumulation poses a significant challenge for breeding efforts. The negative correlation between starch and carotenoid levels in conventional and genetically modified cassava plants implies the absence of a direct genomic connection between the two traits. The competition among various carbon pathways seems to account for this relationship. In this study, we conducted a thorough analysis of 49 African cassava genotypes with varying levels of starch and provitamin A. Our goal was to identify factors contributing to differential starch accumulation. Considering carotenoid levels as a confounding factor in starch production, we found that yellow- and white-fleshed storage roots did not differ significantly in most measured components of starch or de novo fatty acid biosynthesis. However, genes and metabolites associated with myo-inositol synthesis and cell wall polymer production were substantially enriched in high provitamin A genotypes. These results indicate that yellow-fleshed cultivars, in comparison to their white-fleshed counterparts, direct more carbon toward the synthesis of raffinose and cell wall components. This finding is underlined by a significant rise in cell wall components measured within the 20 most contrasting genotypes for carotenoid levels. Our findings enhance the comprehension of the biosynthesis of starch and carotenoids in the storage roots of cassava.

The actin-binding protein palladin associates with the respiratory syncytial virus matrix protein.

The respiratory syncytial virus (RSV) matrix (M) protein plays an important role in infection as it can interact with viral components as well as the host cell actin microfilaments. The M-actin interaction may play a role in facilitating the transportation of virion components to the apical surface, where RSV is released. We show that M protein's association with actin is facilitated by palladin, an actin-binding protein. Cells were infected with RSV or transfected to express full-length M as a green fluorescent protein (GFP)-tagged protein, followed by removal of nuclear and cytosolic proteins to enrich for cytoskeleton and its associated proteins. M protein was present in inclusion bodies tethered to microfilaments in infected cells. In transfected cells, GFP-M was presented close to microfilaments, without association, suggesting the possible involvement of an additional protein in this interaction. As palladin can bind to proteins that also bind actin, we investigated its interaction with M. Cells were co-transfected to express GFP-M and palladin as an mCherry fluorescent-tagged protein, followed by cytoskeleton enrichment. M and palladin were observed to colocalize towards microfilaments, suggesting that palladin is involved in the M-actin interaction. In co-immunoprecipitation studies, M was found to associate with two isoforms of palladin, of 140 and 37 kDa. Interestingly, siRNA downregulation of palladin resulted in reduced titer of released RSV, while cell associated RSV titer increased, suggesting a role for palladin in virus release. Together, our data show that the M-actin interaction mediated by palladin is important for RSV budding and release.IMPORTANCERespiratory syncytial virus is responsible for severe lower respiratory tract infections in young children under 5 years old, the elderly, and the immunosuppressed. The interaction of the respiratory syncytial virus matrix protein with the host actin cytoskeleton is important in infection but has not been investigated in depth. In this study, we show that the respiratory syncytial virus matrix protein associates with actin microfilaments and the actin-binding protein palladin, suggesting a role for palladin in respiratory syncytial virus release. This study provides new insight into the role of the actin cytoskeleton in respiratory syncytial virus infection, a key host-RSV interaction in assembly. Understanding the mechanism by which the RSV M protein and actin interact will ultimately provide a basis for the development of therapeutics targeted at RSV infections.

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations.

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Geminivirus C4/AC4 proteins hijack cellular COAT PROTEIN COMPLEX I for chloroplast targeting and viral infections.

Geminiviruses infect numerous crops and cause extensive agricultural losses worldwide. During viral infection, geminiviral C4/AC4 proteins relocate from the plasma membrane to chloroplasts, where they inhibit the production of host defense signaling molecules. However, mechanisms whereby C4/AC4 proteins are transported to chloroplasts are unknown. We report here that tomato (Solanum lycopersicum) COAT PROTEIN COMPLEX I (COPI) components play a critical role in redistributing Tomato yellow leaf curl virus C4 protein to chloroplasts via an interaction between the C4 and ß subunits of COPI. Coexpression of both proteins promotes the enrichment of C4 in chloroplasts that is blocked by a COPI inhibitor. Overexpressing or downregulating gene expression of COPI components promotes or inhibits the viral infection, respectively, suggesting a proviral role of COPI components. COPI components play similar roles in C4/AC4 transport and infections of two other geminiviruses: Beet curly top virus and East African cassava mosaic virus. Our results reveal an unconventional role of COPI components in protein trafficking to chloroplasts during geminivirus infection and suggest a broad-spectrum antiviral strategy in controlling geminivirus infections in plants.

Fast computation of the eigensystem of genomic similarity matrices.

The computation of a similarity measure for genomic data is a standard tool in computational genetics. The principal components of such matrices are routinely used to correct for biases due to confounding by population stratification, for instance in linear regressions. However, the calculation of both a similarity matrix and its singular value decomposition (SVD) are computationally intensive. The contribution of this article is threefold. First, we demonstrate that the calculation of three matrices (called the covariance matrix, the weighted Jaccard matrix, and the genomic relationship matrix) can be reformulated in a unified way which allows for the application of a randomized SVD algorithm, which is faster than the traditional computation. The fast SVD algorithm we present is adapted from an existing randomized SVD algorithm and ensures that all computations are carried out in sparse matrix algebra. The algorithm only assumes that row-wise and column-wise subtraction and multiplication of a vector with a sparse matrix is available, an operation that is efficiently implemented in common sparse matrix packages. An exception is the so-called Jaccard matrix, which does not have a structure applicable for the fast SVD algorithm. Second, an approximate Jaccard matrix is introduced to which the fast SVD computation is applicable. Third, we establish guaranteed theoretical bounds on the accuracy (in [Formula: see text] norm and angle) between the principal components of the Jaccard matrix and the ones of our proposed approximation, thus putting the proposed Jaccard approximation on a solid mathematical foundation, and derive the theoretical runtime of our algorithm. We illustrate that the approximation error is low in practice and empirically verify the theoretical runtime scalings on both simulated data and data of the 1000 Genome Project.

Statistical methods for cis-Mendelian randomization with two-sample summary-level data.

Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.

A fast linkage method for population GWAS cohorts with related individuals.

Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional linkage methods are computationally inefficient for larger datasets. Here, we describe Population Linkage, a novel application of Haseman-Elston regression as a method of moments estimator of variance components and their standard errors. We achieve additional computational efficiency by using modern methods for detection of IBD segments and variance component estimation, efficient preprocessing of input data, and minimizing redundant numerical calculations. We also refined variance component models to account for the biases in population-scale methods for IBD segment detection. We ran Population Linkage on four blood lipid traits in over 70,000 individuals from the HUNT and SardiNIA studies, successfully detecting 25 known genetic signals. One notable linkage signal that appeared in both was for low-density lipoprotein (LDL) cholesterol levels in the region near the gene APOE (LOD = 29.3, variance explained = 4.1%). This is the region where the missense variants rs7412 and rs429358, which together make up the ε2, ε3, and ε4 alleles each account for 2.4% and 0.8% of variation in circulating LDL cholesterol. Our results show the potential for linkage analysis and other large-scale applications of method of moments variance components estimation.

A cogent technique to circumvent the use of (some) ratio measures in physiology.

Physiologists often express the change in the value of a measurement made on two occasions as a ratio of the initial value. This is usually motivated by an assumption that the absolute change fails to capture the true extent of the alteration that has occurred in attaining the final value - if there is initial variation among individual cases. While it may appear reasonable to use ratios to standardize the magnitude of change in this way, the perils of doing so have been widely documented. Ratios frequently have intractable statistical properties, both when taken in isolation and when analysed using techniques such as regression. A new method of computing a standardized metric of change, based on principal components analysis (PCA), is described. It exploits the collinearity within sets of initial, absolute change and final values. When these sets define variables subjected to PCA, the standardized measure of change is obtained as the product of the loading of absolute change onto the first principal component (PC1) and the eigenvalue of PC1. It is demonstrated that a sample drawn from a population of these standardized measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range; and preserves the rank order of the ratios. It is also shown that this method can be used to express the magnitude of a physiological response in an experimental condition relative to that obtained in a control condition. KEY POINTS: The intractable statistical properties of ratios and the perils of using ratios to standardize the magnitude of change are well known. A new method of computing a standardized metric, based on principal components analysis (PCA), is described, which exploits the collinearity within sets of initial, absolute change and final values. A sample drawn from a population of these PCA-derived measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range as the ratios; and preserves the rank order of the ratios. The method can also be applied to express the magnitude of a physiological response in an experimental condition relative to a control condition.

Evaluation of heritability partitioning approaches in livestock populations.

BACKGROUND: Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium. RESULTS: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively). CONCLUSIONS: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

Decoupled responses of above- and below-ground beta-diversity to nitrogen enrichment in a typical steppe.

Increased atmospheric nitrogen (N) deposition affects biodiversity in terrestrial ecosystems. However, we do not know whether the effects of N on above-ground plant ß-diversity are coupled with changes occurring in the soil seed bank. We conducted a long-term N-addition experiment in a typical steppe and found that above-ground ß-diversity increased and then decreased with increasing N addition, whereas below-ground ß-diversity decreased linearly. This suggests decoupled dynamics of plant communities and their soil seed bank under N enrichment. Species substitution determined above- and below-ground ß-diversity change via an increasing role of deterministic processes with N addition. These effects were mostly driven by differential responses of the above-ground vegetation and the soil seed bank ß-diversities to N-induced changes in environmental heterogeneity, increased soil inorganic N concentrations and soil acidification. Our findings highlight the importance of considering above- and below-ground processes simultaneously for effectively conserving grassland ecosystems under N enrichment.

The Hierarchical Coevolutionary Units of Ecological Networks.

In ecological networks, cohesive groups of species may shape the evolution of interactions, serving as coevolutionary units. Ranging across network scales, from motifs to isolated components, elucidating which cohesive groups are more determinant for coevolution remains a challenge in ecology. We address this challenge by integrating 376 empirical mutualistic and antagonistic networks and coevolutionary models. We identified cohesive groups at four network scales containing a significant proportion of potential direct coevolutionary effects. Cohesive groups displayed hierarchical organisation, and potential coevolutionary effects overflowing lower-scale groups were contained by higher-scale groups, underscoring the hierarchy's impact. However, indirect coevolutionary effects blurred group boundaries and hierarchy, particularly under strong selection from ecological interactions. Thus, under strong selection, indirect effects render networks themselves, and not cohesive groups, as the likely coevolutionary units of ecological systems. We hypothesise hierarchical cohesive groups to also shape how other forms of direct and indirect effects propagate in ecological systems.

Intransitivity in plant-soil feedbacks is rare but is associated with multispecies coexistence.

Although plant-soil feedback (PSF) is being recognized as an important driver of plant recruitment, our understanding of its role in species coexistence in natural communities remains limited by the scarcity of experimental studies on multispecies assemblages. Here, we experimentally estimated PSFs affecting seedling recruitment in 10 co-occurring Mediterranean woody species. We estimated weak but significant species-specific feedback. Pairwise PSFs impose similarly strong fitness differences and stabilizing-destabilizing forces, most often impeding species coexistence. Moreover, a model of community dynamics driven exclusively by PSFs suggests that few species would coexist stably, the largest assemblage with no more than six species. Thus, PSFs alone do not suffice to explain coexistence in the studied community. A topological analysis of all subcommunities in the interaction network shows that full intransitivity (with all species involved in an intransitive loop) would be rare but it would lead to species coexistence through either stable or cyclic dynamics.

Quantifying the contribution of dominance deviation effects to complex trait variation in biobank-scale data.

The proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method to estimate the variation in a complex trait that can be attributed to additive (additive heritability) and dominance deviation (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of additive and dominance heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency [MAF] > 1%) and 50 quantitative traits measured in 291,273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no statistically significant evidence for dominance heritability (p<0.05/50 accounting for the number of traits tested) and estimate that dominance heritability is unlikely to exceed 1% for the traits analyzed. Our analyses indicate a limited contribution of dominance heritability to complex trait variation.

Assessing chromatin relocalization in 3D using the patient rule induction method.

Three-dimensional (3D) genome architecture is critical for numerous cellular processes, including transcription, while certain conformation-driven structural alterations are frequently oncogenic. Inferring 3D chromatin configurations has been advanced by the emergence of chromatin conformation capture assays, notably Hi-C, and attendant 3D reconstruction algorithms. These have enhanced understanding of chromatin spatial organization and afforded numerous downstream biological insights. Until recently, comparisons of 3D reconstructions between conditions and/or cell types were limited to prescribed structural features. However, multiMDS, a pioneering approach developed by Rieber and Mahony (2019). that performs joint reconstruction and alignment, enables quantification of all locus-specific differences between paired Hi-C data sets. By subsequently mapping these differences to the linear (1D) genome the identification of relocalization regions is facilitated through the use of peak calling in conjunction with continuous wavelet transformation. Here, we seek to refine this approach by performing the search for significant relocalization regions in terms of the 3D structures themselves, thereby retaining the benefits of 3D reconstruction and avoiding limitations associated with the 1D perspective. The search for (extreme) relocalization regions is conducted using the patient rule induction method (PRIM). Considerations surrounding orienting structures with respect to compartmental and principal component axes are discussed, as are approaches to inference and reconstruction accuracy assessment. The illustration makes recourse to comparisons between four different cell types.