Guillain-Barré syndrome (GBS) is generally considered to be monophasic, and recurrent GBS (RGBS) is very rare. Pharyngeal-cervical-brachial (PCB) is a less common variant of GBS. There have been no cases reported describing RGBS showing different phenotype presenting as PCB variant with three species of ganglioside antibodies. We report a case of a 77-year-old female patient with GT1a, GD1a and sulfatide-seropositive PCB-GBS after prior episode of AMAN-GBS 13 years ago. Our patient showed oropharyngeal and cervicobrachial weakness associated with areflexia in the upper limbs and partially improved after 5 days of IVIG and physiotherapy.This study reports a rare case characterized as recurrent GBS after a long period, showing different phenotypes in different episodes with three different species of ganglioside antibodies. Further studies are required to obtain better understanding of RGBS and PCB variant. Recurrent Guillain-Barré syndrome (GBS) is a rare condition that reportedly appears in 2–6% of patients with GBS , 2.The cA 77-year-old female presented to the neurology department in our hospital with a 5-day history of dysarthria and upper limb weakness with steady progression. She suffered diarrhea 10 days previously and totally recovered before hospitalization. Further medical history of this patient revealed that this was her second occurrence of GBS. At the age of 64, she suffered the first attack, characterized by dysphagia, dysarthria and bilateral upper and lower limb weakness with areflexia. Weakness was more severe in the lower limbsthan the upper limbs(MRC grade 3/5). A Nerve conduction study(NCS) during her first presentation revealed axonal motor type polyneuropathy. She was diagnosed with acute motor axonal neuropathy(AMAN) according to Hadden’s criteria [The PCB variant of GBS is a rare condition which is characterized by rapidly progressive oropharyngeal and cervicobrachial weakness associated with hyporeflexia or areflexia in the upper limbs . The strGBS is generally considered to be monophasic, but RGBS which is defined as two or more episodes of acute monophasic neuropathy with near or complete recovery between episodes , can occThe clinical manifestations of RGBS are heterogeneous, and the associated risk factors remain unclear. RGBS presenting as the PCB variant is very rare, yet its phenotype remains unfamiliar to many neurologists and general physicians. Our case report highlights the wide heterogeneity of the PCB variants and different phenotypes can occur in the same patient after one decade. Further studies are needed to obtain a better understanding of RGBS and PCB.


With its efficient implementation, dcHiC enables high-resolution compartment analysis as well as standalone browser visualization, differential interaction identification and time-series clustering. dcHiC is an essential addition to the Hi-C analysis toolbox for the ever-growing number of bulk and single-cell contact maps. Available at: https://github.com/ay-lab/dcHiC.The compartmental organization of mammalian genomes and its changes play important roles in distinct biological processes. Here, we introduce dcHiC, which utilizes a multivariate distance measure to identify significant changes in compartmentalization among multiple contact maps. Evaluating dcHiC on four collections of bulk and single-cell contact maps from in vitro mouse neural differentiation ( The organisation of mammalian genomes plays a role in many biological processes. Here the authors report dcHiC, a tool which uses a multivariate distance measure to identify changes in compartmentalisation among multiple genome-wide chromatin contact maps, and apply this to different human and mouse datasets. Since then, many experimental techniques have been developed to image and map chromatin, allowing us to look at chromatin organization at an ever-increasing resolution. The greatest strides in this area have been made in the past decade following the advent of genome-wide conformation capture techniques. We now know that interphase chromosomes are folded into multiple layers of hierarchical structures. Each layer contributes to the establishment and maintenance of the epigenetic landscape that controls cellular state and function.The three-dimensional organization of chromatin in the nucleus has been of interest to scientists for more than a century now. The observation that different chromosomes occupy a defined space in the nucleus dates back to Carl Rabl’s work in animal cells in 188512. In broad terms, two types of compartments divide the genome into regions of open and active chromatin (compartment A) versus inactive and closed chromatin (compartment B)13. Further analysis of each compartment revealed subsets of regions with markedly different properties within each class called subcompartments15 as well as a putative third class (intermediate or I) that is at the interface between A and B and is reorganized in cancer cells9.Among these, the megabase-scale compartmental organization of eukaryotic genomes has been shown to play a critical role in transcription, DNA replication, accumulation of mutations, and DNA methylation17. Briefly, this process involves distance normalization (observed/expected for each genomic distance) of the Hi-C contact map for each chromosome at a particular resolution  followed by transformation into a correlation matrix, where each entry  denotes the correlation of row i and row j (or column i and j since symmetric) of the distance-normalized Hi-C map. The eigenvalue decomposition of the correlation matrix provides eigenvalues and eigenvectors, and typically the first principal component (PC1) derived from them represents the genomic compartments A and B. If PC1 corresponds to chromosome arms or other broad patterns in the Hi-C map , the second principal component (PC2) is likely to represent A and B compartments. The A and B compartment labels are assigned to the positive and negative stretches of the selected PC, respectively. However, depending on the implementation of eigenvalue decomposition, it may be necessary to reorient these assignments correctly using GC content or gene density.The main method to extract compartment information has been to analyze high-throughput chromosome conformation capture (Hi-C) contact maps using Principal Components Analysis (PCA)25, generated across many cell types and conditions26. In addition, single-cell Hi-C datasets now provide a rich test bed for studying cluster-specific and/or time dependent changes in compartmentalization31. Technical challenges such as selecting the correct PC and sign that represents A/B compartments and their scaling across different datasets become larger problems when comparing many bulk or single-cell Hi-C contact maps. Thus far, comparative compartment analysis has been mainly limited to examining compartment flips between two Hi-C maps at a time33.Whether one is interested in the two major compartments or their more nuanced subsets, the principal components derived from PCA have been the major determinants of compartment type. However, standard PCA is limited to analyzing each Hi-C contact map individually, and computational methods that can compare compartmentalization across multiple (>2) Hi-C datasets are needed. This is becoming an obstacle in analyzing the ever-increasing chromatin conformation data, either from Hi-C or its variants34. We follow this by quantile normalization to obtain comparable compartment scores across two or more Hi-C maps/replicates at a time , a method that identifies statistically significant differences in compartmentalization among two or more contact maps, including changes that are not accompanied by a compartment flip. Our method implements a parallelized partial singular value decomposition (SVD) that uses a memory-efficient data structure (Filebacked Big Matrix or FBM) and efficiently computes only the first few singular vectors  that are need for compartment analysisime Fig. , Step 1.n = 3), mouse hematopoiesis (n = 10), a set of lymphoblastoid cell lines (LCLs) from different human populations (n = 20) and single-cell Hi-C data from post-natal mouse cortex and hippocampal brain regions at six different time points clustered into three developmental stages (n = 3). Analyzing each Hi-C dataset at resolutions ranging from 10 Kb to 250 Kb, we identify relevant compartmentalization differences reflecting the underlying biology in the respective scenarios. In the mouse neuronal differentiation model, dcHiC identified compartmental changes for loci involving critical genes associated with cellular identities in mouse embryonic stem cells (mESC) and neuronal differentiation, such as Dppa2/4, Zfp42, Ephb1, and Ptn, as well as GO term enrichments consistent with these cellular identities. In a ten-way comparison (n = 10) of major progenitor and differentiated cell types from mouse hematopoiesis, dcHiC revealed significant compartmental changes involving key genes such as Sox6, Meis1, Runx2, Klf5, and many others. Across both neural and hematopoietic differentiation models, our results also highlight the importance of generally ignored compartmental differences within the same compartment type  that best correlates with gene density and GC content per sample . The resulting compartment scores are quantile normalized, and a multivariate score  is computed based on an initial covariance estimation. We then refine the null distribution by removing outliers before calculating new covariance estimates that will be used for computing the final statistical significance (χ2 test) of differences in compartmentalization . dcHiC provides standalone browser visualization as well as several other features facilitating the interpretation of its results. Figure As more complex experimental designs emerge that compare multiple different Hi-C profiles, a comprehensive method to compare the spatial organization of the genome is necessary. To do this, dcHiC first employs a time- and memory-efficient R implementation of singular value decomposition (SVD) to achieve the eigenvalue decomposition of each Hi-C contact map33), and the CscoreTool17, a method that uses a likelihood function over a sliding window to infer compartment scores. The resulting compartment scores were highly similar among the three methods at 100Kb resolution using mouse ESC Hi-C data, with Pearson’s r = 0.96 between dcHiC and HOMER, 0.97 between HOMER and CscoreTool, and 0.98 between CscoreTool and dcHiC , from which we generated contact maps at 5 different resolutions and 6 different sequencing depths 40. Therefore, we chose these two cell types to demonstrate dcHiC’s utility in a pairwise comparison to replicate known compartment flips and identify additional significant changes that are supported by other data. This also allowed us to compare pairwise differential compartment calls from dcHiC and HOMER.Previous studies have reported substantial compartment flips during the mouse embryonic cell (ESC) to neuronal progenitor cell (NPC) transition, a well-studied in vitro differentiation systemOverall, dcHiC identified 1981 100 kb bins with statistically significant differential compartmentalization (FDR < 0.1), covering up to 7.5% of the genome. For ESC and NPC, ~37% (72.8 Mb) and ~51% (101.6 Mb) of these differences involved A (active) compartment, respectively. The differential compartments are further subdivided into flipping (A->B or B->A) or matching (A->A or B->B) compartment transitions. We observed that ~74% of all the differential compartments were flips  during ESC to NPC transition, whereas the remaining ~26% were within matching compartments Fig. . We furtt-test p-values < 0.05) enriched for regions with higher ESC and NPC differentials for lamin association and replication timing signals although both methods captured regions with signal differences in all three measures. We also performed differential expression analysis between ESCs and NPCs to map the differentially expressed (DE) genes  on the differential compartments. We observed that dcHiC differential compartment bins were enriched in the number of DE genes  differences between ESC and NPC per 100 Kb for the regions from dcHiC and HOMER’s differential calls  and 1,355 of these 100Kb bins overlapped with dcHiC differential calls  and the 3 NPC biological replicates (720M–1.5B reads) at their full sequencing depth (100%) and then down-sampled each replicate separately to 5 different read depths: 80%, 60%, 40%, 20%, and 10%. To profile the effects of down-sampling, we first compared ESC and NPC replicates at each read depth using 100Kb resolution contact maps. We found that there over 80% recall of differential compartments called from full sequencing depth (“ground truth”) for down-sampling rates of 40% or more . First, at 100% sequencing depth, we recovered 1906 differential bins out of 1981 we found from the 4v3 analysis above suggesting a minimal effect of the low depth replicate for differential compartment identification. Next, we observed that over 80% recall for these 1906 bins was kept up until 20% down-sampling  in this 3v3 analysis as opposed to 40% for 4v3 analysis. Overall, these results suggest that replicates with substantially lower sequencing depth than others may not contribute much to overall discovery power of dcHiC especially if they are sequenced below 100 M reads. Our data also shows that with replicates of at least 100 M reads, differential compartment analysis at 25Kb or lower resolution  can be carried out with an acceptable recall of all compartmentalization changes that can be detected with deeper sequencing.dcHiC by running it to compare replicates of the same conditions/sample. We used all four biological Hi-C replicates available for ESC in different combinations . When we ran dcHiC on these combinations, the number of significant compartment changes  ranged from 1 to 32 with a median value of 2 bins , suggesting a low false-positive rate for identifying differential compartments. When we ran the same analysis using 10Kb bins, we identified a median value of 751 differential bins (~0.2% of the genome), suggesting that higher resolution differential analysis may be more prone to false positives.To show the utility of our tool in detecting differences at higher resolution, we ran dcHiC at 10Kb resolution to call differential compartments between ESC and NPC. We found a total of 16,581 10Kb bins (165.81 Mb) differential compartments between the conditions. Among the 1981 100Kb dcHiC differential bins (198.1 Mb), 72% exactly overlapped at least one 10Kb differential bin (over 86% within ±2 bins). This suggests a significant overlap across resolutions but also highlights the prevalence of regions that are detectable only at higher or lower resolution compartment analysis  at different resolutions  and down-sampling rates . We measured the number of differential compartments when running two down-sampled replicates against each other at different resolutions and our results indicate that dcHiC is robust to type-1 error when comparing replicates at different resolutions and read depths (Supplementary Table Zfp42/Rex1 and Dppa2/4) as well as EPH Receptor B1 (Ephb1) and other marker genes specific to neuronal differentiation. Figure Dppa2/4 region in mouse chromosome 16 that is shown to change radial positioning, chromatin state, lamin B1 association and replication timing during differentiation42. Consistent with these data, both dcHiC and HOMER reported a significant shift from the A (active) to the B (inactive) compartment during mouse ESC to NPC differentiation . Dach1 lies in a compartment reported to be flipped from ESC-B to NPC-A by dcHiC  experiments were performed to study changes in radial positioning during the ESC to NPC transition. These included pluripotency markers specifically expressed in ESCs  or chromatin loops , which will then be filtered for their overlap with differential compartments and tested for their difference across the compared conditions. The black square boxes in Fig. Dppa2/4 region. These interactions are identified among FitHiC2 calls46 (FDR < 0.05) that are reported as significant in at least one replicate of ESC and/or NPC datasets. The results show that the Dppa2/4 domain in NPC specifically interacts with its upstream region compared to ESC, while the interactions with the adjacent downstream region remained unchanged, a change that can be visualized on the Hi-C map . This feature allows users to input a set of significant chromatin interactions  adopting a more nuanced representation of the two primary compartment classes. We applied Calder on ESC and NPC Hi-C maps separately and retrieved a total of 24,546 100Kb bins (~2.4 GB) with sub-compartment assignments for both ESC and NPC. For these bins, we then assessed the overlap of differential calls from dcHiC with the differences in sub-compartment labels. Out of 1,981 dcHiC bins, for 1,862 we had Calder labels on both cell types and among those 97.5%  overlapped with differential sub-compartment labels. For the remaining 22,684 bins with Calder labels that do not overlap with dcHiC differential calls, still a high but smaller percentage (57.5%) corresponded to differences in sub-compartment labels. Supplementary Fig. Recent studies have shown that beyond open and closed chromatin, genome activity encompasses multiple states of compartmentalization which can be captured via a more refined sub-compartment analysisRex2) and migration and invasion inhibition (Miip)48.To better understand the type of sub-compartment flips that are overrepresented in dcHiC calls, we compared the transition probabilities among sub-compartment labels (ESC vs NPC) obtained from dcHiC differential calls versus non-differential regions .Although the broad classification of A and B compartments is likely insufficient to capture the multistate genomic activity across cell lines, our sub-compartment analysis suggested that differential analysis using compartment scores is able to effectively capture changes involving sub-compartments with biological significance. Sub-compartment inferring algorithms such as Calder39. This developmental lineage provides an approach to demonstrate how dcHiC uses a multivariate distance measure to compare the compartmentalization of more than two cell types simultaneously. For such multiway comparisons, dcHiC provides a quick and straightforward approach to detect outliers in compartment scores and associated differential interactions, an approach far easier with many experiments than the traditional paradigm of taking pairwise comparisons. In this section, we first illustrate the biological significance of dcHiC’s differential compartments using multiple lines of biological data. We then demonstrate functional term enrichments and show specific differential genes that illustrate the application’s breadth of analysis.The same in vitro system used to differentiate from ESCs to NPCs also allows further differentiation of NPCs to cortical neurons or CNs49, we showed that compartmental dynamics are strongly associated with the variability of gene expression and histone modifications . Figure ons Fig. .Fig. 5Th50 to identify 6 major clusters. Two major clusters corresponded to regions that progressively became more euchromatic (clusters 1 and 6), and one corresponded to more heterochromatic regions (cluster 4). We observed other clusters that corresponded to one cell type showing highly different compartmentalization with respect to the other two . To link these compartmentalization patterns to gene function, we identified genes overlapping with each differential compartment bin for each cluster. Performing functional enrichment analysis on these gene sets51, we identified signatures that are consistent with the cellular identity of the cell type with the highest compartment z-scores . For instance, for the genes overlapping with clusters 1 and 6 with compartment scores increasing from ESC to NPC to CN, the enriched terms included neurogenesis and neuronal development  at a time, we utilized time-series analysis to cluster the compartmentalization score patterns of these differential bins across Fig.  and plotent Fig. . For cluclusters  and 6, aclusters  and 6, aDppa2/4, Zfp42/Rex1 is a well-studied pluripotency marker primarily expressed in undifferentiated stem cells  within the A compartment throughout the ESC-NPC-CN transition, for which the variation in the expression profile strongly correlated with changes in compartmentalization  differential compartment bins across the ten cell types encompassing many of the genomic regions previously shown to undergo hematopoiesis-related dynamic changesNext, similar to the ESC-NPC-CN transition, we also carried out functional enrichment analysis of differential regions with the highest A compartment score in each group and specific cell type. Figure 58. Figure 58 also reported a set of critical genes for hematopoietic lineage differentiation. We identified 12 of these 26 genes within differential compartments  with gradual increase or decrease in their compartment scores  to Granulocytes (GR) (6 time-points) and LT-HSC to Megakaryocytes (MK) (7 time-points) lineage differentiation separately. For LT-HSC to GR differentiation, the first 3 clusters show a general pattern of differential compartments with decreasing genomic activity while the last 3 shows an increase  derived from 13 Yoruban individuals, one Puerto Rican trio, one Han Chinese trio, and one European LCL (GM12878). They measured significant differences in 3D genome organization across individuals using different metrics, including the Directionality Index (DI), Insulation Score (INS), Frequently Interacting REgions (FIREs), and compartment scores66. The study also carried out differential analysis of compartments across individuals and provided both compartment scores and “variable regions” at 40 kb resolution . To minimize technical variation and ensure a fair comparison, we started directly from the 40 Kb compartment scores reported by Gorkin et al. and ran dcHiC on these values . dcHiC allows direct utilization of precomputed compartment scores, such as in this case, when available.Measuring the extent to which genetic variation across individuals influences chromatin features, including 3D organization, has significant implications in our understanding of human disease. Previous studies have revealed that the presence of variations such as quantitative trait loci (QTLs) can affect histone modifications, transcription factor binding, and enhancer activity across populations65 were mapped on differential compartments identified from dcHiC and by Gorkin et al.66. Using the non-differential compartment regions as background for each method, we observed nearly no enrichment for regions called differential only by Gorkin et al.  identified by dcHiC. The NR2F2 region was investigated using FISH by Gorkin et al., which confirmed individual-specific changes in 3D chromatin conformation. Two of the individuals from the cohort (YRI-4 and YRI-8) showed enriched interaction between the NR2F2 FISH and another placed upstream compared to YRI-3 and YRI-5. The variability of 3D genome organization among individuals is also apparent from compartment scores for this region. The NR2F2 locus across the cohort was found to be a part of a strong B-compartment for all Yoruban individuals except for YRI-4, YRI-8, and YRI-9 . They employed diploid chromatin conformation capture (Dip-C) method and generated over 3k single-cell Hi-C maps from cortex and hippocampus encompassing 6 different time points that comprehensively describe the dynamic 3D genome organization. This high-quality, time-course and single-cell resolution Hi-C data provided us with an opportunity to showcase the expansion of dcHiC’s utility in performing differential analysis on the pseudo-bulk single-cell Hi-C data. Tan et al. 2021 comprehensively described neuronal sub-types from the single-cell 3D genome data and most of their analysis focused on comparisons among the sub-types. Here we performed differential compartmental analysis among the time points to demonstrate the utility of dcHiC in identifying dynamic changes in compartmentalization for both brain regions. To perform the differential analysis, we first categorized the 6 time points from each brain region into three groups, namely – Early , Mid , and Late  as shown in the Fig. Tshz2, Vip, Sst, Sox11, Tubb2b, Nrep, and Syt1 were also found to be part of dcHiC-identified dynamic changes  is structurally related to neuregulin 1 (NRG1)71, which plays a critical role in controlling the growth and differentiation of glial, epithelial and muscle cells72. The expression of Nrg3 is known to be highly restricted within developing and adult nervous system. Tissue-specific differential compartments encompassing important genes included the region containing Chrm5, which encodes a muscarinic cholinergic receptor that binds acetylcholine and was found within a differential compartment in hippocampus but not in cortex  also helped us identify significantly differential compartments falling into similar dynamic patterns  for each brain region. The Fig. Sox11  is known to be expressed in developing neurons75 and plays critical role in learning and memory formation76. We found this gene as part of a specific differential compartment in cortex that gradually changes from B in early to A in late stages of development. The left panel in Fig. Cntnap4 gene in the hippocampus following cluster 1’s pattern shown in Fig. Cntnap4 is part of the common set of regions that are differential in both brain regions. The right panel in Fig. Dennd1a,a protein coding gene known to be involved in vesicle-mediated transport pathways77 and Rab regulation of trafficking78. Although highly expressed in neuronal as well as glial cells of the brain, the specific role of this gene in the hippocampus remains to be investigated.Example genes from differential compartments among different stages of post-natal mouse brain development: Fig. r 1 Fig.  in corteOverall, these results showed that dcHiC addresses a need in the differential analysis of single-cell Hi-C data by first utilizing pseudo-bulk profiles from a low number of cells (80–251) to characterize compartmentalization of each condition, and then systematically comparing multiple conditions such as timepoints or clusters with the same approach we use for the bulk cell Hi-C data. Our analysis here offers an example scenario where comparing multiple conditions with replicates  was essential to identify important known and new genes and to characterize dynamic patterns shared across different regions.This paper presents an application, dcHiC, to compare compartmentalization across Hi-C datasets. dcHiC facilitates comparative analysis across multiple contact maps and helps identify biologically relevant compartmentalization differences with statistical confidence scores. Along with conventional pairwise differential analysis, dcHiC allows a single multivariate differential comparison of Hi-C datasets, utilizing replicates when available, and provides an efficient approach to analyze multiple Hi-C maps without the need for generating many different combinations. In terms of its approach, dcHiC first identifies principal components efficiently using a parallelized partial singular value decomposition (SVD). It then uses quantile normalization on the resulting compartment scores followed by computation of a multivariate distance measure to systematically identify significant compartmentalization changes among multiple contact maps. Through an extensive set of technical comparisons and analyses, we showed that dcHiC enables high-resolution compartment analysis in a more time-efficient manner compared to the existing approaches. Using replicates and pseudo-replicates, we show that dcHiC has a low type-1 error rate and the identified differential compartments are fairly robust to varied levels of sequencing depths and resolutions.79. Our initial analysis showed that over 10% of all significant compartment differences we found between ESC and NPC belong to nucleolus-associated domains (NADs) that were deemed exclusive to either ESC or NPC79, providing an explanation for a subset of differences in compartmentalization during differentiation. In the same pairwise analysis, we also found that differential compartments are enriched for sub-compartmental flips (changes in subcompartment labels) especially with an enrichment of strong heterochromatin ESC sub-compartments transitioning to euchromatin sub-compartments in NPC as compared to the background. Such changes were also associated with strong alterations in the transcriptional activity of overlapping genes.We applied dcHiC to various biological scenarios, ranging from neuronal and hematopoietic stem cell differentiation in mice to Hi-C data from different human populations along with pseudo-bulk single-cell Hi-C maps of cells from cortex and hippocampus during post-natal mouse brain development. Our results confirmed that dcHiC detects known compartmental changes among cell types, including those previously shown to play a role in neuronal and hematopoietic differentiation. When comparing dcHiC to existing approaches, we showed that it identifies regions with higher differences in replication timing, Lamin B1 signals, differentially expressed genes, and histone marks suggesting a better prioritization of relevant biological changes. Even though differences in compartmentalization between ESCs and NPCs are generally aligned with changes in the lamin B1 association, a recent work highlighted the importance of nucleolus association in revealing layers of compartmentalization with distinct repressive chromatin statesn = 3) comparison of cell types during in vitro mouse neuronal differentiation and showed that dcHiC continues to systematically identify critical marker genes and can recover cell-specific functions from differential compartment analysis alone. Across dcHiC’s differential compartments, we observed significant and relevant enrichment of biological processes such as neuron differentiation in NPC and CN cells. More broadly, dcHiC’s differential compartments also compellingly aligned with changes in Lamin B1, gene expression, and histone modification data. Taken together, these results demonstrate dcHiC’s ability to find regions with the most biologically relevant changes in compartmentalization across the genome.We next expanded our analysis to a three-way (n = 10), provided a unique opportunity to demonstrate a number of different utilities of dcHiC. A ten-way multivariate differential comparison of the hematopoietic system revealed previously known lineage-specific critical genes overlapping differential compartments. Notably, we identified vital transcription factors, such as Sox6, Runx2, Meis1, Foxo1, and many other critical genes, such as Abca13, by solely analyzing the differential calls. Our functional enrichment analysis of gene sets overlapping with the lineage-specific differential compartments reported from the apex to the bottom of the hematopoietic model tree reconfirmed that genome compartments play a contributory role in determining the accessibility of genes in specific cell types. We also performed time-series analysis for two different lineages (LT-HSC to GR and LT-HSC to MK) leading to different compartment change patterns some of which were gradual increase or decrease and several others were driven uniquely by the megakaryocyte–erythrocyte progenitor (MEP) state.The hierarchical mouse hematopoietic stem cell differentiation model, consisting of ten different cell types with Hi-C data (n = 20) also highlighted our method’s utility and strength. Most dcHiC calls overlapped with a subset of variable compartments reported by the previous study, but dcHiC calls were enriched for higher variability in a number of different biological signals. Similarly, the regions encompassing Frequently Interacting Region (FIRE)-QTLs and Directionality Index (DI)-QTLs defined by the previous study were more enriched in the top differential compartment calls of dcHiC than in the top calls defined in the previous study. The analysis also demonstrated an important feature of dcHiC: the ability to directly utilize previously computed compartment scores to run differential compartmentalization analysis.Measuring the extent of compartment variability across twenty-cell human types  demonstrated the challenges introduced by different technical and biological factors in reliably calling and comparing A/B compartments across single cells. Here, by studying a recent single-cell Hi-C (Dip-C) data characterizing post-natal dynamics of mouse brain development in two brain regions, we showed that dcHiC is able to call compartments and perform differential analysis from pseudo-bulk profiles of as low as 80 cells per condition. We observed a higher number of differential compartments in the cortex region compared to the hippocampus. The differential compartments were overlapping with cell-type specific marker genes and previously known variable genes identified through single-cell transcriptomic analysis. The time series clustering of the differential compartments across three stages  of development helped us identify other important genes in differential compartments that were not captured in the original paper. Although we highlighted the use of dcHiC for time course analysis of single-cell data, the same can be applied to any predefined set of clusters of cells either with respect to their functional annotations (cell type or subset) or sample conditions .80) derived from any organism with compartmental genome organization. dcHiC is also readily applicable to comparative analysis of other coarse-grain (10 kb to 1 Mb resolution) genome-wide signals such as replication timing and lamin association. With hundreds of publicly available Hi-C datasets in the 4D Nucleome Data Portal and others published every day, dcHiC will play an essential role in the comparative analysis of high-level genome organization. As single-cell Hi-C data start providing better resolution for compartment analysis, dcHiC and its future extensions will be critical to enable compartment comparison across thousands of cells and tens of conditions, clusters or cell subsets.The framework we developed here provides a systematic way to identify differential compartments and visualize these differences in different scenarios, including multiway, hierarchical and time-series settings. Although we focused on human and mouse Hi-C data in this work, our method is readily applicable to Hi-C data or its variants  across all samples mapped to the hg19 reference genome66. Statistically significant interactions were called using FitHiC246 with default parameters and an FDR threshold of 0.05 for each replicate and/or each sample.All the Hi-C data, except for Gorkin et al., were mapped to the mm10 reference genome and processed using the HiCpro (v2.7.9) pipeline39 study concerning mouse neural development were processed using our in-house and open-source RNA-seq processing pipeline (https://github.com/ay-lab/LJI_RNA_SEQ_PIPELINE_V2.git), which utilizes STAR82. The differential gene expression analysis between mouse ESC and NPC cell lines (two replicates each) was performed using the DESeq2 method41 with all the default parameter settings.The RNA-seq data from Bonev et al.83 and generated the corresponding bam files (MAPQ > 20). The aligned files were then used as input to the MACS2 program84 to call peaks  against their respective input controls. The continuous ChIP-seq peaks were then merged, and the unique set was mapped to the 100 kb differential compartments to calculate the average number of peaks. The enrichment of signal difference was calculated by first quantifying the absolute difference in signal  within ESC to NPC differential and non-differential compartments. The enrichment of absolute signal difference between the differential and non-differential compartments between ESC and NPC was then compared by unpaired T-test.For ChIP-seq peak calling , we first mapped the respective fastq files to the mm10 genome using bowtie250. For gene-term enrichment analysis, the differential compartments are scanned against the gene coordinates of the respective genome defined by the user using the ‘bedtools map’ function85. The unique overlapping set of genes was then extracted and used for GO biological function enrichment analysis using the ToppGene suite API function or directly from their webserver51.Time-series clustering was generated using the TCseq packagedcHiC generates a JavaScript-based stand-alone dynamic IGV-HTML page to visualize the compartments and differential compartment calls, with an option to add additional tracks.bigstatsr R package34. The input to SVD is K different distance-normalized chromosome-wise correlation matrices To perform principal component analysis (PCA) on Hi-C maps, dcHiC utilizes the singular value decomposition (SVD) implementation of the The matrices 86 on the set The eigen-decomposition of the In the case of samples with replicates, dcHiC performs the above steps by including each replicate from each sample . dcHiC then calculates the average quantile normalized values of each genomic bin across all replicates of a given sample to represent sample-wise compartment scores.87. The Mahalanobis distance of a point z-score among the z-scores of all samples for z-score for point Mahalanobis distance (MD) is a multivariate statistical measure of the extent to which the multivariate data points are marked as outliers based on a Chi-square distributionHere,Essentially, the approach provides more weight to the points that are distant from others among the samples  than to points that are closer together in the multidimensional space . Equation  is the scovrob function of the R package robust, which implements the Minimum Covariance Determinant (MCD) procedure that has been shown to improve multivariate outlier detection88. We use a predefined upper-tail critical value of the chi-square distribution with nd pass). The significance of the corresponding nd pass) is calculated from the critical chi-square distribution table as pchisq function of the R programming language followed by multiple testing correction to retrieve adjusted p-values.To increase the sensitivity of our difference detection, we implemented an outlier removal step that eliminates genomic bins (or points) with high MD (as computed above) at the initial pass (1st pass). For calculation of the covariance matrix, we utilize the ough Eq. ) one morp-values.In the case of samples The covariate is calculated as follows:The weighted centers Here, the variables are defined as similar to Eqs.  and 8),,8), and p-values using FDR correction obtained from This approach provides more weight to the features that are closer to each other within replicates of a sample  and as opposed to the calculation across different samples (Eq. ), where ‘pairtobed’ function. dcHiC utilizes the χ2 distribution table as pchisq function embedded within the R programming environment followed by FDR correction to retrieve adjusted p-values.Using the same Mahalanobis distance (MD) measure, dcHiC enables the user to find differential interactions across samples that are either linking two differential compartments together or a differential compartment with other parts of the same chromosome. The goal of this feature is to provide more information on the chromatin organization changes related to or correlated with compartmental differences. For this analysis, we used FitHiC2 to call significant interactions (FDR 5%) for each sample or replicate (when available), but users are free to provide their own set of interaction or loop calls from any other tool. Using these calls, dcHiC first finds the interaction subset that overlaps with differential compartments (on either end or both) using the bedtools No statistical method was used to predetermine sample size, and no data was excluded from the main body of the text. The Investigators were not blinded to allocation during experiments and outcome assessment. We utilized all biological replicates available to us where possible, except in one location specified in the manuscript: when testing the runtime and memory usage of dcHiC, where having evenly sized input samples helped make data collection more uniform.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2


Decisions during adulthood set the foundation for healthy aging, but descriptions of healthy and unhealthy decision processes are missing. We extracted latent profiles of daily decision resources (energy and affect) and linked them to daily leisure activity. Diary data was collected from working adults  over the ten workdays (N=693). We identified three daily decision profiles consistent with the Decision Triangle – (1) logical , (2) automatic , and (3) visceral  – and one additional profile, (4) mild visceral . Daily logical decision-making related to more “want” leisure activities  and the greatest variety in leisure activities. Automatic engaged in the most chores. Visceral engaged in the fewest social activities and least variety in leisure activities. Our findings advance understanding of specific decision processes during leisure, which may have consequences for health and well-being as a person ages.


Previous studies have showed that single nucleotide polymorphisms (SNPs) might be implicated in the pathogenesis of osteosarcoma (OS). Numerous studies involving SNPs with OS risk have been reported; these results, however, remain controversial and no comprehensive research synopsis has been performed till now.This study seeks to clarify the relationships between SNPs and OS risk using a comprehensive meta-analysis, and assess epidemiological evidence of significant associations.The PubMed, Web of Science, and Medline were used to screen for articles that evaluated the association between SNP and OS susceptibility in humans before 24 December 2021. Furthermore, we used Venice Criteria and a false positive report probability (FPRP) test to assess the grades of epidemiological evidence for the statistical relationships.APE1 rs1760944, BCAS1 rs3787547, CTLA4 rs231775, ERCC3 rs4150506, HOTAIR rs7958904, IL6 rs1800795, IL8 rs4073, MTAP rs7023329 and rs7027989, PRKCG rs454006, RECQL5 rs820196, TP53 rs1042522, VEGF rs3025039, rs699947 and rs2010963, VMP1 rs1295925, XRCC3 rs861539), moderate for 14 SNPs in 12 genes and weak for 14 SNPs in 11 genes.We extracted useful data based on 43 articles, including 10,255 cases and 13,733 controls. Our results presented that 25 SNPs in 17 genes were significantly associated with OS risk. Finally, we graded strong evidence for 17 SNPs in 14 genes with OS risk (In summary, this study offered a comprehensive meta-analysis between SNPs and OS susceptibility, then evaluated the credibility of statistical relationships, and provided useful information to identify the appropriate candidate SNPs and design future studies to evaluate SNP factors for OS risk. VEGF rs2010963, ERCC rs1800795, and IL6 rs1800795, have been found to be related to the susceptibility of lung cancer, gastric cancer, and OS (Osteosarcoma (OS) is one of the most common bone malignancies, occurring mainly in the metaphyseal area around the knee joint , 2. Alth, and OS . GeneticVEGF rs3025039 could increase the risk of OS in the recessive model and allelic model  were followed in our study , 20. We The included criteria were as follows: (a) evaluation of the association between SNP and OS susceptibility in a case-control in humans; (b) pathologically confirmed OS; (c) providing sufficient information (such as genotype amount); (d) published articles with full text in English. The excluded criteria were as follows: (a) duplicate publications; (b) case reports, reviews, letters, conference abstracts, and meta-analysis; (c) the articles were about the survival/mortality rate of OS.https://www.ncbi.nlm.nih.gov/snp/). The quality of included studies was assessed based on the Newcastle Ottawa Scale  and co-first author (JT) independently extracted relevant information and then cross-checked each other. If any disagreement was found, discussion and reexamination were made with the third investigator (HD). The following details were extracted: first author, year of publication, variation in gene (rs numbers), gene name, genotype counts, ethnicity, and sample size. Apart from that, three ethnicities  were mentioned in this research; “overall” indicated two or more. Regarding the same SNP with different modes of presentation, we adopted the most recent one on the website (P < 0.05 (two-sided) indicated the significance level in our study. We performed meta-analysis under three models  with at least two independent datasets; a subgroup analysis on the basis of ethnicity was also evaluated if necessary. We assessed the heterogeneity of the different studies using the Cochran’s Q test and the I2 statistic. Specifically, the I2 values were assigned at three levels: ≥50%, 25%–50%, ≤25% (PQ > 0.1) and the random effect model (PQ < 0.1). Furthermore, we conducted sensitivity analysis to evaluate whether significant ORs were lost by excluding the first published study or studies that deviated from the Hardy-Weinberg equilibrium (HWE) in controls. Potential publication bias and small study bias were assessed using Begg’s test and Egger’s test respectively .for the Venice Criteria and FPRP). Cumulative epidemiological evidence of statistical relationships was assigned as strong level  or weak level (any C), or moderate level (a combination of A or B) based on Venice Criteria. Ultimately, evidence levels were adjusted according to the FPRP value .The Venice Criteria and FPRP were respectively used to investigate the cumulative epidemiological credibility of significant relationships , 18, 25 Our research included 5,151 relevant publications, excluded 4,821 papers based on the title and abstract, and excluded 247 papers after a full text review. Apart from that, four papers were screened from reference publications Figure 1APE1 rs1760944, ERCC3 rs4150506, HOTAIR rs7958904, IL8 rs4073, MTAP rs7023329, MTAP rs7027989, PRKCG rs454006, RECQL5 rs820196, VEGF rs2010963, VEGF rs3025039, VEGF rs699947, XRCC1 rs25487 and XRCC3 rs861539 had significant association with the susceptibility to OS in Asians under three models . BCAS1 rs3787547 was significantly associated with susceptibility to OS in Asians . CTLA4 rs231775 had statistical relationship with OS susceptibility in overall population and in Asians under three models. For CTLA4 rs5742909, it was statistically associated with OS susceptibility under recessive model in Asians. We found that ERCC3 rs4150441 had significant association with OS risk under dominant model in Asians. We found that HOTAIR rs874945 was statistically associated with OS susceptibility in Asians under allelic model. IL10 rs1800896 was significantly associated with risk of OS in overall population . We found that IL6 rs1800795 had significant association with OS risk in Asians . TNF-α rs1800629 had statistical association with OS susceptibility in overall population under three models. Our results presented that TP53 rs1042522 had statistical association with OS susceptibility in overall population , and in Caucasians . For VEGF rs1570360, our results showed that SNP rs1570360 had statistical association with OS susceptibility in Asians under allelic model. VEGF rs833061 had statistical relationship with OS susceptibility in Asians under allelic model. VMP1 rs1295925 had statistical relationship with OS susceptibility in overall population ; of these, 25 SNPs in 17 genes were statistically associated with susceptibility to OS ; BCAS1 rs3787547 (two associations); CTLA4 rs231775 (four associations); ERCC3 rs4150506 (one association); HOTAIR rs7958904 (one association); IL6 rs1800795 (one association); IL8 rs4073 (one association); MTAP rs7023329 (two associations); PRKCG rs454006 (two associations); RECQL5 rs820196 (one association); TP53 rs1042522 (two associations); rs3025039, rs699947, and rs2010963 in VEGF (eight associations); VMP1 rs1295925 (one association); XRCC3 rs861539 (one association). 16 associations between 15 SNPs in 13 genes and OS risk obtained FPRP 0.05 to 0.2. 20 associations between 16 SNPs in 13 gens and OS risk obtained FPRP value >0.2. Finally, 31 associations with strong evidence were found between 17 SNPs in 14 genes and OS risk (APE1 rs1760944 (two associations); BCAS1 rs3787547 (two associations); CTLA4 rs231775 (four associations); ERCC3 rs4150506 (one association); HOTAIR rs7958904 (one association); IL6 rs1800795 (one association); IL8 rs4073 (one association); rs7023329, rs7027989 in MTAP (three associations); PRKCG rs454006 (two associations); RECQL5 rs820196 (one association); TP53 rs1042522 (two associations); rs3025039, rs699947, and rs2010963 in VEGF (eight associations); VMP1 rs1295925 (two associations); XRCC3 rs861539 (one association). 17 associations with moderate evidence were found between 14 SNPs in 12 genes and OS risk; 17 associations with weak evidence were found between 14 SNPs in 11 genes and OS risk.We used the Venice Criteria to assess cumulative epidemiological credibility of significant associations  was found only in one association (VEGF rs3025039 under the dominant model in Asians). After deleting the first published study, the relationships between three SNPs in two genes and OS susceptibility were no longer significant  between 23 SNPs in 17 genes and OS risk obtained mild heterogeneity; 7 associations (10.77%) between six SNPs in five genes and OS risk obtained moderate heterogeneity; two associations (3.08%) between two SNPs in two genes and OS risk obtained high heterogeneity Table 1.APE1 rs1760944 (two associations); BCAS1 rs3787547 (two associations); CTLA4 rs231775 (four associations); ERCC3 rs4150506 (one association); HOTAIR rs7958904 (one association); IL6 rs1800795 (one association); IL8 rs4073 (one association); rs7023329, rs7027989 in MTAP (three associations); PRKCG rs454006 (two associations); RECQL5 rs820196 (one association); TP53 rs1042522 (two associations); rs3025039, rs699947, and rs2010963 in VEGF (eight associations); VMP1 rs1295925 (two associations); XRCC3 rs861539 (one association).Our study conducted a comprehensive and updated meta-analysis of the relationships between genetic variants and OS susceptibility. We conducted meta-analysis from 43 papers with 46 SNPs in 21 genes, and found that 25 SNPs in 17 genes were significantly associated with susceptibility to OS (65 significant associations). We further assessed the levels of epidemiological evidence for significant associations combining Venice Criteria as well as FPRP test. Finally, 31 associations with strong epidemiological credibility were found between 17 SNPs in 14 genes and OS risk, as follows: APE1) gene may be involved in the specific activation of DNA repair and numerous malignancies (Oct-1), thus reducing APE1 mRNA expression levels and then decreasing the risk of OS  gene resides in a region at 20q13.2 and BCAS1 rs3787547 may be related to the development of OS by altering the binding power of p53, which is one of the most critical tumor suppressors . The excision repair cross-complementation 3 (ERCC3) gene encodes a DNA helicase that plays an important role in nucleotide excision repair. The polymorphisms of ERCC3 have been reported to be associated with several cancers, such as colorectal cancer, pancreatic cancer, breast cancer, and OS , dominant model (moderate evidence), and recessive model (weak evidence). The HOX transcript antisense RNA (HOTAIR) gene is highly expressed in a variety of cancers, and deletion of HOTAIR can inhibit the aggressiveness of cancers , and HOTAIR rs874945 increases the risk of OS in Asians under the allelic model with weak evidence.The apurinic/apyrimidinic endodeoxyribonuclease 1  gene encodes an inflammation cytokine and may be involved in key steps of tumor proliferation, apoptosis, angiogenesis, and differentiation  gene plays a critical role in both the pathogenesis and progression of many human tumors. IL8 rs4073 is known to affect IL8 expression that regulates cancer progression through mitogenic and angiogenic factors  gene encodes an enzyme that saves methionine and adenine in polyamine metabolism. Inhibition of MTAP expression may be responsible for the development of tumor and MTAP polymorphisms were associated with some cancer risk, including OS  and rs7027989  were associated with a lower risk of OS in Asians. Although the exact mechanism of SNP rs7023329 affect OS risk remains unknown, Zhi et al. hypothesized that SNP rs7023329 might coexist in linkage disequilibrium with one certain variants and affect its regulation machinery to associate with OS risk (PRKCG) gene is located on chromosome 19q13.42 and functions as the major receptor for tumor promoters. Missense variants in exon 4 (C114Y/G123R/G123E) of the PRKCG gene have a relationship with tumor development and migration  gene is mapped on 17q25.1 and encodes a helicase protein that is essential for genome stability. The RECQ family plays a critical role in DNA repair and transcription. Therefore, RECQL5 variants are considered candidate genes for human cancers . However, the mechanism of how SNP rs820196 affected OS risk has not been revealed. The vacuole membrane protein 1 (VMP1) gene encodes a transmembrane protein that plays a key regulatory role in the autophagy process and acts as a tumor suppressors  gene encodes a protein that repairs DNA damage and maintains chromosome stability. XRCC3 polymorphisms influence human cancer susceptibility by altering DNA repair efficiency  gene encodes a protein that transmits an inhibitory signal to T cells, and plays an important role in increasing cancer susceptibility  among all populations and Asians both under allelic model and dominant model. However, no significant association was found among Caucasians, which suggesting that more studies were required to evaluate the relationships among Caucasians. The tumor protein p53 (TP53) gene is located on chromosome 17p13.1 and acts as a tumor suppressor (TP53 rs1042522 (G > C) increased OS risk under recessive model in a small number of Caucasians (98 cases and 67 controls)  and the dominant model (strong evidence) among overall population, as well as in the allelic model (moderate evidence) and the dominant model (weak evidence) among Caucasians. The decrease in OS risk of SNP rs1042522 may be due to the encoding of a protein isomorph that induces transcription and apoptosis of the target gene (VEGF) gene encodes an angiogenesis cytokine, which induces proliferation and migration of vascular endothelial cells, and the genetic variants of VEGF are correlated with tumor risk ; VEGF rs699947 decreased OS risk with strong evidence under three models in Asians. Although each SNP mentioned above contains more than two datasets, the sample size is still small, and the population involved is mainly Asian, suggesting that we need to do more research on large populations and different ethnicities in the future. Interestingly, a study performed by Wang et al.  . Howeverget gene . The vasmor risk . As our g et al.  revealedOur research also found that there were 17 relationships between 14 SNPs in 12 genes and OS susceptibility with moderate evidence, and 17 relationships between 14 SNPs in 11 genes and OS susceptibility with weak evidence. Furthermore, large prospective studies should be performed to elucidate the relationships with OS risk for these SNPs with moderate or weak evidence. Additionally, our study that analyzed the same SNP from different groups reported controversial conclusions due to the genetic models, race, and sample size.Some unavoidable limitations should be noted: (i) although the extensive literature was searched, some papers may have been overlooked; (ii) There could be publication bias because only English articles are examined; (iii) subgroup analysis was conducted based on race (main in Asians and Caucasians) and genetic models, which decrease the credibility of some results; future study in much larger sample size and more races may be needed; and (iv) the errors or confusion of the major allele and the minor allele in the original articles could not be avoided. Therefore, large prospective studies are recommended to evaluate the relationship between OS susceptibility and these SNPs, and all results of our meta-analysis should be interpreted with caution until the molecular properties have been clarified.Collectively, our research evaluated the cumulative evidence of significant associations of genetic variants with OS risk combining the Venice Criteria and the FPRP test to increase the persuasion and precision of the results. 17 variants in 14 genes with 31 associations were rated as strong evidence of OS susceptibility, 14 SNPs in 12 genes with 17 associations were moderate, and 14 SNPs in 11 genes with 17 associations were weak. Our findings provided useful information to identify the appropriate candidate SNPs and design future studies to evaluate the factors of SNPs for OS risk.The original contributions presented in the study are included in the article/WZ and HD contributed to the conception and design of the study. DY and JT performed the literature search, extracted the data, conducted the meta-analysis, and wrote the first draft of the manuscript. YX, NB, and FK contributed to manuscript editing. XF contributed to the manuscript review. WZ and HD contributed to supervise study, manuscript revision. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Prostate cancer (PCa) is the most diagnosed and second deadliest cancer among men in the United States, with an estimated 268,490 new cases and 34,500 deaths in 2022 . While the prognosis for men with early-stage disease remains extremely favorable , men diagnosed with metastatic PCa have a 30% 5-year OS, clearly demonstrating a need for therapeutic options for these patients . Due to a strong reliance on androgens to drive PCa, first and second courses of therapy involve androgen deprivation therapy or targeting the androgen receptor directly in combination with several other cytotoxic agents –3. UnforOf interest, mitochondria are emerging as critical organelles that promote tumorigenesis and metastasis . Mitochoin vivo models, with MIRO2 expression strongly correlating with growth of PCa tumors. To determine how MIRO2 supported PCa growth and survival, we identified novel MIRO2 binding partners through co-immunoprecipitation mass spectrometry. We explored the top hit from our screen, General Control of Non-derepressible (GCN1), and found that GCN1 was also crucial for the growth and survival of PCa cells. GCN1 acts as the upstream activator of GCN2—one of the major kinases involved in the integrated stress response (ISR)—which functions to sense a variety of intracellular stresses including amino acid availability, redox stress, and actin dynamics cues [Our work identified that patients with recurred/progressed disease had higher MIRO2 mRNA expression and higher MIRO2 expression correlated with worse disease-free survival for these patients. Rigorously using cell models representing androgen sensitive and androgen insensitive disease, we found that MIRO2 was universally critical for PCa cell growth and survival. Importantly, this was replicated in xenograft ics cues , 22. IntThis study adds to an ever-growing body of data that highlights the importance of mitochondria—outside their traditional roles as metabolic hubs—as important signaling nodes to promote tumor growth. It is interesting to note that ablation of MIRO1 had no effect on PCa growth, survival, or stress signaling in response to nutrient deprivation, providing solid evidence of non-overlapping functions in these contexts. This is in contrast to recent studies performed in mouse embryonic fibroblasts, which require loss of both MIRO1 and MIRO2 to affect mitochondrial functions, which suggests that MIRO1 and MIRO2 are functionally redundant or cooperate to maintain cellular health , 20. WheAnother major discovery was the identification of many previously unknown binding partners for MIRO2. Interestingly, many canonical MIRO2 interactors that have been characterized in non-tumorigenic cells, were not found in our screen. This gives rise to the possibilities that the function of MIRO2 could be completely different in 1) cancer vs. normal cells or 2) stressed vs. non-stressed cells. Furthermore, while the interaction of MIRO2 and GCN1 was readily found in many PCa cell lines and tumor samples from patients, this interaction was almost completely lost in normal immortalized cell lines or normal prostate epithelia from patient samples. This suggests that targeting the MIRO2-GCN1 axis would have limited off target effects towards normal cells in patients.in vivo [in vivo models of acute lymphocytic leukemia, strong anti-proliferative effects have been observed when used in combination with asparaginase (ASNase)-dependent depletion of asparagine and glutamine in serum [in vivo xenografts tumors—treatment with GCN2 inhibitors alone may be enough to induce anti-proliferative effects.Our research also found that solid tumors established in mice strongly induced the activation of GCN2. This is consistent with new research studies that also show GCN2 to be a critical sensor of mitochondrial health and for PCa tumor growth , 24. Excin vivo , 26. Whiin serum , 26. In Overall, this research proposes a new paradigm on how AR-independent PCa can drive tumor traits by exploiting mitochondrial signaling pathways and offers a fresh opportunity to develop novel targeted therapies for patients with prostate cancer.


Tubulin is a protein that plays a critical role in maintaining cellular structure and facilitating cell division. Inhibiting tubulin polymerization has been shown to be an effective strategy for inhibiting the proliferation of cancer cells. In the past, identifying compounds that could inhibit tubulin polymerization has required the use of in vitro assays utilizing purified tubulin or immunofluorescence of fixed cells. This study presents a novel approach for identifying tubulin polymerization inhibitors using a CRISPR-edited cell line that expresses fluorescently tagged β-tubulin and a nuclear protein, enabling the visualization of tubulin polymerization dynamics via high-content imaging analysis (HCI). The cells were treated with known tubulin polymerization inhibitors, colchicine, and vincristine, and the resulting phenotypic changes indicative of tubulin polymerization inhibition were confirmed using HCI. Furthermore, a library of 429 kinase inhibitors was screened, resulting in the identification of three compounds  that inhibit tubulin polymerization. Live cell tracking analysis confirmed that compound treatment leads to rapid tubulin depolymerization. These findings suggest that CRISPR-edited cells with fluorescently tagged endogenous β-tubulin can be utilized to screen large compound libraries containing diverse chemical families for the identification of novel tubulin polymerization inhibitors. Cancer constitutes the second most common cause of death following cardiovascular diseases worldwide . DespiteOf the many approaches used to treat cancer, chemotherapy is one of the most common and effective tools used by oncologists . In chemMicrotubules composed of alpha and β-tubulin heterodimers that wind together to form a hollow cylinder of 24 nm in diameter . These sMicrotubule targeting agents interact with tubulin via at least four different binding sites: laulimalide, taxane/epothilone, vinca alkaloid, and colchicine . PolymerWhile tubulin polymerization inhibitors have made great progress in the treatment of many forms of cancer, they have several drawbacks, including hematopoietic and neurologic toxicities, inconvenience formulation, and resistance. Therefore, identifying novel tubulin polymerization inhibitors is necessary to combat these barriers .Traditionally, compounds that are inhibitors of tubulin polymerization have been found using pure tubulin in vitro  or proceThis work demonstrates the use of a cell line with endogenous tagging with fluorescent proteins of β-tubulin and histone H1 to identify novel tubulin polymerization inhibitors. We screened a library of kinase inhibitors and identified and characterized three compounds as tubulin polymerization inhibitors using high-content imaging (HCI) analysis of live cells.HeLa cells with endogenous tagging with fluorescent proteins of β-tubulin (mClover3), histone H1 (mTagBFP2), and p62-SQSTM1 (mRuby3) were developed with CRISPR and the FAST-HDR vector system, and were recently described . These c3 cells per well and 16 h later were treated with the library compounds at a final concentration of 1 uM using the PerkinElmer JANUS Automated Workstation. The compounds’ effects were assessed 24 h later with an Operetta CLS Confocal High Content Imaging System  and the following parameters: excitation and emission filter combinations: mTagBFP2 , mClover3 ; acquisition time: 500 ms per channel. Two images (with two different channels) were acquired with the same coordinates for each well using a 40X water immersion objective. On average, between 60 and 100 cells were analyzed per well. As negative controls 32 wells in each 384-well plate were only treated with dimethyl sulfoxide (DMSO) at 1% final concentration.The CRISPR-edited HeLa cell line described above was used to screen a small compound library of 429 kinase inhibitors  for high-content imaging drug screening. The positive control compounds (colchicine and vincristine) as well as the identified hits in this study  were acquired for further validation from Cayman Chemicals in powder form and dissolved with DMSO. All confocal images in this work were acquired with live cells grown in Dulbecco’s Modified Eagle Medium (DMEM) without phenol red  supplemented with 10% Fetal Bovine Serum (100 uL). The 384-well CellCarrier Ultra plates  were seeded with 5 × 102), and images were acquired every three minutes with the same filter combinations described above. All conditions were analyzed in triplicates, and the experiments were repeated three independent times. All compounds under study were administered 30 min after the start of image acquisition in a concentration range between 1 nM and 4 uM.For time-lapse microscopy, cells were kept under environmentally controlled conditions . The analysis was conducted using the following steps within the Analysis module of Harmony Software: (1) identifying the cell nucleus with the “Find Nuclei” algorithm using the Histone H1-mtagBFP2 channel, (2) detecting the cytoplasm boundaries of the identified cell nucleus using the “Find Cytoplasm” algorithm with the TUBB-mClover3 channel, and (3) analyzing the texture properties of the cytoplasm of each cell using the Haralick analysis option with default settings. The Haralick texture analysis option provides values for pixel correlation, contrast, homogeneity, and sum variance. For this study, we only evaluated changes in the mean pixel homogeneity values per each well.bos taurus (protein data bank code: 4O2B) for molecular docking experiments. This bos taurus tubulin isoform has a 100% homology to human β-tubulin (TUBB). The blind molecular docking was performed with CB-Dock version 2, a cavity detection-guided protein–ligand blind docking web server [We used the x-ray diffraction crystallography protein structure of β-tubulin from b server  that useb server  and Maesp values < 0.05 were considered statistically significant. Differences between control and treated samples in time-dependent experiments were found using two-way analysis of variance within GraphPad Prism.All figures in this work are representative of at least three independent experiments. The comparison of multiple groups was performed using one-way analysis of variance with Dunnett’s test within GraphPad Prism (version 9) ; The analysis of phenotypic changes caused by compounds that alter tubulin polymerization usually requires the use of antibodies, chemical staining, or promoting the overexpression of fluorescent tubulin . HoweverIt was then identified how these cells respond to tubulin polymerization inhibitors by first treating the cells with two different tubulin polymerization inhibitors: vincristine and colchicine. Phenotypic changes were identified, where the normal pattern of the microtubules disappears in treated cells B. Next, The HeLa cell line with endogenous tagging of histone H1 and β-tubulin was used for screening a library of 429 kinase inhibitors to identify tubulin polymerization inhibitors using HCI analysis. The cells were plated onto 384 well plates, treated with the library of kinase inhibitors for 16 h, and directly analyzed with an automated high-content imager A. The ob50 dose required to find phenotypic changes in tubulin polymerization. First, the optimal amount of time required to detect tubulin polymerization inhibition of modified HeLa cells treated with colchicine was determined by conducting time-lapse imaging for 16 h. This showed that the tubulin polymerization inhibition process is fast and can be detected in the first three hours of treatment. The Haralick homogeneity values of cells treated for three hours were obtained with the three hits compounds and colchicine in a concentration range between 1 nM and 4 μM. The validation showed that the three compounds can inhibit tubulin polymerization  compared to the three compounds. Notably, a recent report has described that colchicine has an IC50 value of 10.65nM for inhibiting the polymerization of purified microtubules in vitro [To confirm that ON-01910, HMN-214, and KX2-391 are inhibitors of tubulin polymerization, a dose response analysis was performed for detecting the ICrization A. It was HMN-214 B. Additiin vitro . Our resNext, it was evaluated how rapidly ON-01910, KX2-391, and colchicine affected tubulin polymerization by tracking treated cells over a period of three hours by acquiring images in the two fluorescent channels every three minutes. After analyzing the time-lapsed images with the routine described in Another aim was to evaluate the potential interaction of the hit compounds  with β-tubulin and to confirm that the effect of tubulin polymerization inhibition is achieved by directly blocking tubulin function. We evaluated the potential interaction of these compounds with β-tubulin using molecular docking, which is used to predict the predominant binding mode of a ligand with a protein of known three-dimensional structure .bos taurus, which has 100% sequence identity with human β-tubulin (TUBB). The predicted interactions match the location of experimental x-ray crystallography structures  was performed using the three-dimensional structure of β-tubulin from ructures A. Followructures B. The moructures A. We fouructures B. A receMicrotubules play a crucial role in several biological processes, including cellular shape maintenance, cell motility, and mitosis . Due to This work describes a simplified process to identify tubulin polymerization inhibitors with live cells. The method relies on the use of a CRISPR-modified cell line that contains fluorescent proteins tagged to endogenous β-tubulin and histone H1. This cell line allows the direct use of high-content imaging analysis for cellular segmentation and microtubule network detection without requiring cell fixing or immunostaining. With this method, the three compounds  were identified in a group of 429 kinase inhibitors as tubulin polymerization inhibitors. Previous reports that used alternate methodologies validate these findings for two of these compounds. ON-01910 was reported to show tubulin destabilization activity after comparing changes in cells with genome-wide upregulation/downregulation via a CRISPR activation/inhibition approach and treated with this compound . In addi50) kinase inhibitors of their respective target, while HMN-214 is an inactive prodrug that requires liver metabolic transformation before becoming a kinase inhibitor [Two of the three compounds identified in this study (ON-01910 and KX2-391) are potent (<10 nM ICnhibitor . The capThe blind molecular docking analysis indicates that these three compounds can interact directly with the colchicine binding site of β-tubulin, thus suggesting a similar mechanism of action for inhibiting tubulin polymerization. The blind molecular docking was done using CB-Dock version 2, a cavity detection-guided protein–ligand blind docking web server . The curThis study validates the simple use of a CRISPR-edited cell line for identifying tubulin polymerization inhibitors via high-content imaging analysis. This method is considerably cheaper and less laborious than previous methods due to the elimination of the required steps for cellular fixing and immunostaining. Additionally, this method allows live cell analysis of tubulin polymerization for evaluating kinetic changes. We foresee the use of cell lines with endogenous tagging of tubulin with fluorescence proteins for screening larger and diverse libraries for discovering novel tubulin polymerization inhibitors. We also anticipate that this type of cell lines could be good models to validate novel tubulin polymerization inhibitors discovered by virtual compound screening.


The glymphatic system has been described recently as a series of perivascular channels that facilitate fluid exchange and solute clearance in the brain. Glymphatic dysfunction has been implicated in numerous pathological conditions, including Alzheimer’s disease, traumatic brain injury, and stroke. Existing methods for assessing glymphatic function have been challenging: dynamic methods, such as two-photon microscopy and contrast-enhanced magnetic resonance imaging require expensive instrumentation and specific technical skills; slice-based fluorescent imaging is more readily implemented but lacks temporal resolution.in vivo in mice.To develop a straightforward and adaptable dynamic imaging approach for assessing glymphatic function Using a widely available small animal infrared (IR) imaging system (LICOR Pearl), visualization of IR cerebrospinal fluid tracer distribution over the cortical surface enables time-resolved measurement of the dynamics of glymphatic exchange. Using co-injection of IR and conventional fixable fluorescent tracers, dynamic imaging can be paired with whole-slice fluorescence imaging, permitting the quantification of glymphatic function throughout the brain as well as subsequent histological assessment.These techniques were validated against one another, comparing differences between animals anesthetized with ketamine/xylazine and isoflurane.This technique permits sensitive dynamic imaging of glymphatic function, with the concurrent visualization of resolution of deeper structures. Supported by astroglial aquaporin-4 water channels lining perivascular pathways, the glymphatic system facilitates cerebrospinal fluid (CSF) and interstitial fluid (ISF) exchange through the brain, removing interstitial solutes and waste products along peri-venous pathways.–,,,,–in vivo imaging of tracer movement across time but have important limitations. Two-photon and MRI require expensive equipment and technical expertise that are not always readily available or affordable. In addition, two-photon imaging enables high-resolution imaging of tracer movement but can only image a very small field-of-view and often necessitates the use of invasive cranial windows that may affect normal glymphatic flux. MRI provides a wide field of view but cannot resolve down to microstructures. While these approaches provide sensitivity to time, they are limited by their anatomical scale and often require the access and use of expensive equipment.Glymphatic exchange is most frequently assessed using slice-based fluorescent imaging after injection of fixable fluorescent tracer into the cisternal CSF.Using the LICOR Pearl Trilogy small animal imaging system, we have developed a straightforward and novel dynamic imaging paradigm for visualization of infrared (IR) tracer distribution across the cortical surface in mice as a surrogate measure of glymphatic flux within the brain parenchyma. This approach is readily combined with fluorescence-based slice imaging to visualize tracer movement throughout brain tissue in addition to concurrent histological examination. This approach enables the combination of sensitive dynamic imaging over time and resolution of intraparenchymal structures that can be assessed in combination with histological targets of interest. Further, it uses affordable and widely available equipment, allowing for facile replication and widespread adoption of the technique.2in vivo imaging was complete, animals were perfusion-fixed, and IR and fluorescent surface imaging were performed on extracted whole brains. To validate this imaging technique against previously established slice-based methods of glymphatic imaging,–The purpose of this study was to develop and validate a novel dynamic imaging modality to quantify perivascular glymphatic exchange through the mouse brain in real time. A cocktail of two fluorescent tracers, including an inert IR carboxylate dye  and a fluorescently labeled dextran , was injected into the cisterna magna, and tracer movement was imaged every 2 min from 20 to 40 min post-infusion using the LICOR Pearl Trilogy small animal imaging system. After 2.1ex vivo whole-brain surface imaging [The primary goal was to determine if an IR CSF tracer could be dynamically imaged with the LICOR Pearl IR imaging system, allowing for assessment of tracer movement over the surface of the cerebral convexity as a surrogate measure of glymphatic exchange. The experimental pipeline shown in  describe imaging  and trac imaging . We also imaging .,,,Supplemental Material show representative imaging time series with dynamic tracer movement from ,,–,Ex vivo brain surface images showed higher IR tracer intensity on the ventral surface compared to the dorsal surface in animals anesthetized with both Iso and K/X [,To determine whether this dynamic imaging technique could detect differences in perivascular glymphatic exchange, we evaluated well established effects of different anesthesia regimes on glymphatic function. Ketamine/xylazine (K/X) anesthesia modulates noradrenergic signaling and enhances glymphatic function,st (ROI)  were selst (ROI) . Consist and K/X , which i2.2Post hoc analysis revealed that the effect of K/X anesthesia on CSF tracer influx was distributed throughout the slices within the cortex, but differences in subcortical structures were driven by changes in the posterior brain. This post hoc analysis found no significant effect of anesthesia in the corpus callosum across slices. No significant effect of anesthesia was observed on CSF tracer influx into the hippocampus. As an alternative approach, we measured regional changes in threshold-based area coverage of CSF tracer fluorescence . While this approach resulted in similar anesthesia-based differences in glymphatic influx, we observed that measurement of mean regional fluorescence resulted in less variable data.Next, we tested how these dynamic IR and surface imaging results compared to the method of slice-based quantification of fluorescence intensity, which has been a standard technique used to assess glymphatic function. A cocktail of IR carboxylate dye and fluorescent tracer was injected into the cisterna magna, allowing for direct comparison of fluorescence with IR imaging. Stereoscopic fluorescence imaging of the dorsal and ventral brain surfaces showed higher CSF tracer fluorescence on the ventral compared to the dorsal surface  Guide for Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee of the University of Washington, Seattle.4.2Dual IR (non-fixable) and paraformaldehyde (PFA) fixable fluorescent CSF tracer was prepared with Texas red-dextran  at a concentration of 0.5% and 4.3To visualize tracer movement from the subarachnoid space of the cisterna magna into the brain parenchyma, mice were placed on the heated LICOR Pearl imager platform. Animals in the 2% Iso anesthesia treatment group were maintained under 2% Iso, while K/X treated animals received an additional 1/3 dose of K/X by weight 45 min after the first K/X dose to maintain the plane of anesthesia. Transcranial images were taken of the exposed skull surface every 2 min from 20 min post tracer injection to 40 min post tracer injection. Imaging from 20 to 40 min captures tracer influx into the mouse cortex, however, longer imaging timepoints could be used depending on the outcome of interest. In the present study, we sought to conduct perfusion fixation at 45-min post-injection, thus dynamic imaging ended at 4.4After 40 min of tracer influx and imaging, mice were transcardially perfused with ice-cold phosphate-buffered saline (PBS), followed by 4% PFA. Brains were removed, and images of the dorsal and ventral surfaces of the extracted brain were taken on the LICOR. Brains were then post-fixed overnight in 4% PFA at 4°C, protected from light. Brains were rinsed in PBS and cryoprotected in 30% sucrose until saturated.4.5ex vivo sections rostral to caudal through the brain with a Keyence BZ-X800 fluorescence scope. Tile scans were taken at Once brains are appropriately cryoprotected in 30% sucrose, dorsal and ventral surface fluorescent and bright-field images were acquired using fluorescent stereoscope images. Brains were then fixed in optical coherence tomography and maintained at 4.6Additional representative immunofluorescence was performed on frozen K/X treated Once antibody staining was complete, the section was mounted as described above and imaged at 4.7Supplemental Material.Stitched fluorescent images were processed in ImageJ. Overlay images were RGB separated, and the DAPI/blue channel was used to manually draw ROI for the cortex, corpus callosum, hippocampus, and sub-cortical regions as shown in 4.8All statistical testing was performed on GraphPad Prism 9.2 and was assembled in Adobe Illustrator. Tests were chosen based on the data set being analyzed and are reported in the figure legends. They include repeated-measures two-way ANOVA, Sidak’s multiple comparisons test, and Pearson’s correlation. All statistical testing was two tailed, and exact Click here for additional data file.