The 3D genome of plasma cells in multiple myeloma

The 3D genome of plasma cells in multiple myeloma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The 3D genome of plasma cells in multiple myeloma Kaiji Zhang, Mengsi Chen, Ming Chen, Yue Wang, Haibo Liu, Yanju Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5663072/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jun, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract Multiple myeloma (MM) is a hematological malignancy characterized by expanding clonal plasma cells in the bone marrow (BM) that produce monoclonal immunoglobulin. It is an incurable disease, accounting for about 10% of blood malignancies and the second most common hematologic malignancy. Therefore, in-depth research into the molecular mechanisms and therapeutic targets of the disease is crucial. For the first time, we performed high-throughput chromosome conformation capture (Hi–C) analysis of plasma cells in five multiple myeloma patients, and integrated it with genome resequencing and transcriptomic associated with genomic variation and gene expression. As a result, 19 specific TAD (Topologically Associating Domain) boundaries in MM samples related to the immune response and Wnt signaling pathways were identified. Additionally, Loop structures were also analyzed, revealing that promoter-enhancer-associated loops were the most prevalent. Genomic characteristics of MM patients were explored, identifying SNPs, InDels, and CNVs, with variations in the CDS region potentially affecting gene function. Transcriptome analysis showed differentially expressed genes in MM patients, mainly involved in p53 signaling and cell adhesion. Multi-omics analysis identified overlapping genes related to MM, including those involved in MHC class II protein complex assembly and antigen presentation. The study provides insights into the complex genomic and transcriptomic changes in MM plasma cells, potentially aiding in identifying therapeutic targets. Biological sciences/Cancer Biological sciences/Molecular biology Health sciences/Medical research Health sciences/Molecular medicine 3D genome Genome resequencing Transcriptome Plasma cell Multiple myeloma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Multiple myeloma (MM) is a form of cancer that predominantly affects adults and is centered around the excessive multiplication of plasma cells within the bone marrow 1 . It is an incurable disease, and most patients experience relapse/refractory 2 , posing significant challenges for patients and healthcare providers. However, targeted immunotherapeutic strategies such as CAR-T, CAR-natural killer (NK), and TCR-T have emerged as a beacon of hope in the treatment of MM due to their potential to minimize harm to healthy tissues 3 , 4 . Recent applications of chromosome conformation capture techniques, particularly Hi-C, have revolutionized our understanding of the 3D cancer genome 5 . Studies using Hi-C data from different species have shown that the switching of chromatin compartments between active (A) and inactive (B) states is closely linked to changes in gene expression 6 – 8 . In prostate cancer, topologically associating domains (TADs) are reduced in size and exhibit alterations at key tumor suppressor loci, such as TP53 9 . Previous studies have shown that the destruction of TAD boundaries will lead to the regulation of the genes and the occurrence of diseases 10 , 11 . Therefore, the TAD boundary plays a very important role. Deleting the boundary section will cause a disorder of gene regulation, resulting in the transcription of originally silenced genes, while those that should have been transcribed were silenced 12 . The boundaries of TAD can not only guide the folding of chromosomes into higher structures, but also correctly guide the remote transcriptional regulation 13 . Moreover, the spatial architecture of the genome actively shapes the nature of cancer-associated genomic alterations 14 . These insights reveal a bidirectional relationship between the 3D organization of the genome and the genetic changes that drive cancer 15 . Understanding this interplay is crucial for unraveling the molecular mechanisms of cancer and for developing targeted therapies that consider the spatial context of genomic alterations 16 . A previous study found that in the context of multiple myeloma (MM), the three-dimensional (3D) organization of the genome undergoes significant changes when compared to normal B cells 5 . These alterations are characterized by an increase in the number of topologically associating domains (TADs), a reduction in the average size of these domains, and a shift in the chromatin state of certain genomic regions 5 , 11 . Multi-omics analysis is an important method to explore the formation of traits and the development of diseases 5 , 17 . However, limited multiple myeloma study was explored using 3D genomics methods based on the different patients. Despite considerable progress, the development of extensive 3D datasets that capture the cancer genome's structure is still in its infancy. Therefore, in-depth research into the disease's molecular mechanisms and therapeutic targets is crucial. Plasma cells are a specialized type of white blood cell that plays a vital role in the immune system. They are derived from B lymphocytes, a type of immune cell that produces antibodies. 3D genomics is an emerging field of study that focuses on the spatial organization of the genome. However, the 3D genomic study of plasma cells is limited. Characterizing the cancer genome's spatial disarray and its functional implications is paramount, especially considering the prevalence of genomic changes such as mutations in cancer. In our study aimed at unraveling the molecular characteristics of MM cancers, we employed a multi-faceted analysis. We utilized Hi-C contact maps, WGS, and RNA-seq data from 5 patients and 1 control normal person ( Table S1 ) to scrutinize potential biases in Hi-C data. Our findings unveiled a significant link between the architectural features of the 3D genome, genomic variations, gene expression, and MM cancer. This integrated analysis enhances our comprehension of the 3D cancer genome's role in MM and opens new avenues for therapeutic intervention by targeting genomic disarray. Results Clinical information about the patients The cohort of this study consists of individuals aged between 44 and 80, with both male and female participants ( Table S1 ). The group includes five MM patients, each with distinct treatment regimens. The treatments range from combinations of bortezomib, thalidomide, and dexamethasone to more complex therapies involving pomalidomide, bortezomib, dexamethasone, daratumumab, and selinexor. The time of diagnosis varies, with the earliest in August 2018 and the latest in July 2023. Genetically, the patients exhibit a range of chromosomal abnormalities. For instance, several individuals have FISH results showing 1q21 amplifications and deletions of 13/13q. One patient has a complex karyotype with multiple abnormalities, including deletions, translocations, and aneuploidy, while another shows a highly abnormal karyotype with numerous structural and numerical changes. In contrast, one patient has normal FISH and karyotype results. Overall, this group is characterized by significant genetic heterogeneity and diverse treatment approaches, reflecting the complexity of their conditions. In addition, all MM patients were seriously ill and died within 2 years after diagnosis, these samples had certain characteristics and were difficult to collect. With this clinical context in mind, we next explored the 3D genomic architecture of MM plasma cells to understand how chromatin organization might contribute to disease progression. Dynamic changes in compartmentalization and local accessibility To elucidate the multiscale rewiring of chromatin architecture and its influence on MM plasma cells, we used in situ Hi-C to map chromatin contacts for plasma cells of five patients (MMC1, MMC2, MMC3, MMC5, and MMC6) and one control sample (Control). As a result, the average clean data of the sample is 180.37 Gb. We then generated a total of 1.45 billion valid contacts (with an average of 242.28 million contacts per sample ( Tables S2 ) and reached a maximum resolution of 8.15 kb ( Tables S2 ). Most (57.18%) contacts occurred within chromosomes and consisted of the dominant (87.61%) long-range interactions ( Tables S2 ). We then constructed genome-wide contact maps by dividing the genome into 500 kb regions (Fig. 1 A, Figure S1 A ). Inter‑chromosomal interactome indicated that the chromosomes with similar lengths have a similar likelihood to mutually contact each other, which was consistent with the previous study 5 . All samples showed a strong decrease in contact probability with an increase in the distance between loci (Fig. 1 B). Compartment A, rich in actively transcribed genes, features open chromatin with histone marks for active transcription and is centrally located within the nucleus. In contrast, Compartment B, with fewer genes and inactive transcription, has closed chromatin with marks for gene silencing and is peripherally situated in the nucleus 8 . These compartments are crucial for genome organization and gene regulation 18 . We identified the compartment state of each sample (Fig. 1 C) and identified substantial levels of compartmental switching in plasma cells across control and 5 MM patients (Fig. 1 D). In these regions, 408 bins were switching from B to A (which contained 259 genes) and 321 bins from A to B (contained 274 genes) (Fig. 1 E). Functional enrichment analysis demonstrated that genes embedded in regions experience the A-to-B switching event and were primarily involved in signaling receptor activity and G protein-coupled receptor activity (Fig. 1 F). This includes molecular transducer activity, transmembrane signaling receptor activity, interleukin-1receptor activity and detection of chemical stimulus involved in sensory perception (Fig. 1 F). Nonetheless, genes located in regions that were subject to B-to-A switching events were primarily involved in DNA-binding transcription factor activity and RNA polymerase II transcription regulatory region sequence-specific DNA binding molecular function, it is related to the development of organs and system of animals. In addition, it is also enriched in type III interferon signaling ( Figure S1 B ). Most TADs were highly stable Having established compartment-level changes in chromatin organization, we next examined the stability and alterations of topologically associating domains (TADs) in MM plasma cells to identify specific regulatory units associated with disease development. At the sub-megabase scale, the local chromatin architecture can be characterized by TAD 19 . A topologically associating domain (TAD) is a highly self-correlated continuous region where the interactions between fragments tend to be more within the TAD than between TADs, and it is separated from its neighbors by distinct boundaries to form an independent regulatory unit that presents a square structure on the heat map diagonal. As a regulatory unit, genes in TAD share common regulatory elements, so there are cooperative expression characteristics of genes in TAD (providing basis for co-expression of adjacent genes on chromosomes). According to the Inclusion ratio (IR), we identified 3197 to 3988 TADs in the six samples (Fig. 2 A), with an average of 702.45 Kb length (Fig. 2 B, Tables S2 ). We next calculated the genome-wide DI (Directionality index score) values of the samples to explore the differential TADs among all samples. Then, the TAD boundaries of all samples are merged. As a result, compared with the control, we found 19 specific TAD boundaries (which embedded 43 genes) in the MM samples. These genes were mainly involved in “immune response”, “neutrophil degranulation”, “leukocyte degranulation”, “leukocyte activation” and “immune system process” biological process terms (Fig. 2 C); “Human immunodeficiency virus 1 infection”, “Wnt signaling pathway”, and “Cytokine − cytokine receptor interaction” KEGG pathways (Fig. 2 D); “molecular function regulator”, “catalytic activity, acting on a protein”, and “guanyl − nucleotide exchange factor activity” molecular function terms ( Figure S2 A ). Figure 2 E showed an example fragment of 10Mb on chromosome 1 with resolution of 40Kb. These results indicate altered immune response due to formation of new and disappearance of the original TAD boundaries associated with multiple myeloma development. Building on the TAD analysis, we next explored chromatin loop structures to understand how promoter-enhancer interactions might contribute to gene expression changes in MM. Global rewiring of loops in multiple myeloma If the interaction frequency of a pair of chromosomal sites is higher than the interaction frequency of the adjacent chromosome segments on the linear line, then the pair is called a significant interaction site (which is also called loop) 20 , that shows the presence of strong interaction signal points at non-diagonal locations on the heat map. The existence of loop structure is the biological reason for the emergence of significant interaction sites, so we can identify significant interaction sites and identify loop structure through Hi-C data. The ends of the loop are referred to as the anchor points of the loop, which include common promoter-enhancer interaction (PEI) sites. We next identified significant interacting sites through Hi-C data and identifying loop structures. As a result, we identified 1069 loops from MMC6 to 6929 loops in control ( Table S2 ). If a loop where one anchor is in the promoter region (the 2 kb upstream of the gene transcription start site serves as the promoter region), and the other anchor is located in a non-promoter region (potential enhancer-like region) is referred to as a promoter-enhancer associated loop (PEL). Finally, we identified the greatest number of loop types was promoter-enhancer associated loops. Most loops were not anchored enhancers or promoters (Fig. 3 A-C), and the number of each loop type shows a significant difference; given that patients MMC1 and MMC2 (normal karyotypes) have different karyotypes compared to patients MCC4, MMC5, MMC6 (complex karyotypes, Table S1 ), it is presumed that MM patients with complex genomic variants lose more regio-interactions in the genome. The differential loop analysis showed 10 specific loops in the 5 MM samples compared with the control (Fig. 3 D). These differential loop-related genes (if a bin size region upstream and downstream of the differentially loop-related boundary intersects with the promoter region of the gene) were further used to perform GO/KEGG enrichment analysis. These genes were mainly involved in “anterior/posterior pattern specification” biological process terms (Fig. 3 E); “Epstein − Barr virus infection”, “Autophagy – animal”, “Relaxin signaling pathway”, and “ECM − receptor interaction” KEGG pathways (Fig. 3 F); “regulatory region nucleic acid binding”, and “chromatin DNA binding” molecular function terms ( Figure S2 B ). These findings indicate the potential role of chromatin loops in autophagy events in MM patients. Genomic characteristics of the MM With insights from chromatin loops, we then turned to the genomic characteristics of MM to identify specific mutations and structural variations that might drive disease progression. To explore the genomic characteristics of MM patients, we determined the whole genomes of 5 patients, compared them with one control person, and identified the SNPs and other variations (Fig. 4 A). As a result, we totally identified 6.58 M, 4.40 M, and 2.18 M SNPs, transitions, and transversions, respectively ( Tables S3 ). In total, there’re 3,636,606 SNPs were identified in the intergenic region (55.28%), and only 45,438 SNPs (0.69%) were identified in CDS regions ( Table S4 ). Of these SNPs in CDS region, most of them were nonsynonymous (49.62%) and synonymous coding SNPs (49.23%) (Fig. 4 B). Similar to SNPs, small InDel annotation results also showed that most InDels were in intergenic regions (51.17%) ( Table S5 ). We also detected the structure variation (SV) of the MM samples. The greatest number of SV (69,169) was found in MMC2 ( Table S6 ), most of them were inversion (65,308). Copy number variation (CNV) detection results showed that the control has the least number of CNV (1067). These variations in the CDS region might cause the function change of the genes, compared with the reference genome, we found there were 6752, 927, and 1211 genes with non-synonymous SNP, InDel, and SV, respectively, for MMC1 patients ( Table S7 ). There are 7765, 12,497, 7957, 8266, and 8034 genes with variations (including Non-synonymous SNP, InDel and SV) in MMC1, MMC2, MMC4, MMC5, and MMC6 respectively. Among these genes, 4691 of them were shared among these five MM patients (Fig. 4 C). These overlapped variated genes were further used to perform GO/KEGG enrichment analysis. These genes were mainly involved in “multicellular organismal process”, “response to stimulus”, and “developmental process” biological process terms ( Figure S3A ); “ECM proteoglycans”, “Collagen formation”, “Collagen chain trimerization”, and “Diseases of glycosylation” Reactome terms (Fig. 4 D); “ECM-receptor interaction”, “Olfactory transduction”, “Graft-versus-host disease”, and “Taste transduction” KEGG pathways (Fig. 4 E); “small molecule binding”, “ion binding”, and “protein binding” molecular function terms ( Figure S3B ). These results implicate SNPs, InDels and SVs in the development of MM were associated with binding processes. Transcriptome changes in MM plasma cells To further link genomic variations to gene expression changes, we performed transcriptome analysis to identify differentially expressed genes and pathways in MM plasma cells. We sequenced five samples (including Control, MMC1, MMC2, MMC4 and MMC6). RNA-seq results showed that after sequencing quality control, a total of 34.01 Gb of clean data was obtained ( Table S8 ). According to the expression of the samples (Fig. 5 A), the correlation analysis showed that these MM patients showed more similar expression pattern compared with control (Fig. 5 B). Compared with control, we identified 1,619 (1,209 up and 410 down regulated genes) differentially expressed genes (DEGs) in the four MM patients (Fig. 5 C, D). These DEGs were mainly involved in “p53 signaling pathway”, “Various types of N − glycan biosynthesis”, “Pathways in cancer”, “Cell adhesion molecules” and “N − Glycan biosynthesis” KEGG pathways (Fig. 5 E); “protein disulfide isomerase activity”, “ADP binding” and “AMP binding” GO molecular function terms ( Figure S4A ); “negative regulation of cell adhesion”, “negative regulation of cell communication”, and “negative regulation of signaling” GO biological process terms ( Figure S4B ). These results indicate a large proportion of perturbations in the transcriptome of MM blood with the majority of dysregulated protein-coding genes associated with adenylate binding. Tumors are intricate conglomerates of both malignant and non-malignant cells. The tumor purity—defined as the proportion of cancer cells within a sample—can complicate integrative analyses by introducing variability 21 . Conversely, it also presents an opportunity to study tumor heterogeneity, offering insights into the complex interplay between cancer cells and their surrounding microenvironment 22 . We then used PUREE 23 to calculate the tumor purity of these MM samples. As a result, the MM samples had tumor purity values ranging from 0.46 (MMC1) to 0.58 (MMC4), which belonged to mid-high (0.38–0.97) purity range samples 23 . The mixture of the genomes of tumor cells and normal cells, which may be responsible for the large number of mutations in the genome. Multi-omics analysis Integrating the multi-omics data, we explored the overlap between genomic variations, chromatin organization, and gene expression to identify key genes and pathways that may drive MM development. We first checked the overlapped genes among differentiated compartments, TAD, and loop (Fig. 6 A). Three hundred and fifty overlapped genes were found in DEGs and genomic CDS variated genes (Fig. 6 B). These genes were mainly involved in “MHC class II protein complex assembly” and “Antigen processing and presentation of exogenous peptide antigen” GO biological process terms (Fig. 6 C); “fibronectin binding”, and “MHC class II protein complex binding” GO molecular function terms; and “Autoimmune thyroid disease”, “Cell adhesion molecules”, “N-Glycan biosynthesis”, “Intestinal immune network for IgA production” and “Hematopoietic cell lineage” KEGG pathways (Fig. 6 C). In addition, we found that genes such as NSG2 , ALDH1A2 , and CALCRL showed a higher expression in the MM patients. ARHGAP24 showed a higher expression in the control (Fig. 6 D). NSG2 is a member of the neuron-specific gene (NSG) family, which is specifically expressed in neurons 24 . Cytogenetic analysis has unveiled a complex landscape of genetic mutations in multiple myeloma (MM), with the majority of these alterations being concentrated in structural rearrangements and copy number variations (CNVs) 25 . Among these, the most commonly observed CNVs include gains of chromosome 1 and loss of chromosome 17 26,27 . Here, consistent with previous studies, we also found that the most commonly observed CNVs include gains of chromosome 1 and losses of chromosome 13 were detected ( Table S9 ). We also found that CNV had an important effect on the loops difference in MM patients. Among the 161 loops differentiated between control and MM patients, 141 of them were related to genes (39% of these loops were related to CNV genes). These genes include LRRC63 , GPR183 , OBI1 , POU4F1 , ZIC5 , COG3 , UBAC2 , TM9SF2 , GPR18 , and ZIC2 . For most of these genes, decreasing number of interactions were detected, the interactions were lower in MM than the control, with an average − 5.73 of log2FC. These results indicate the functional relevance of each level of the 3D genome hierarchy (such as compartment, TAD and loop), and the variations of genome and establishment of 3D genome provides multiple regulatory layers to alter gene expression in MM development. Discussion Chromatin conformation capture techniques, such as 3C, 4C, 5C, Hi-C, and ChIA-PET, have recently been developed to explore the three-dimensional (3D) genome organization of genomes at high-resolution 7 , 19 and reveal gene regulation mechanisms 11 . Using these techniques, many studies have found that the mammalian and bird' genomes are organized into gene-dense and transcriptionally active compartment A and gene-sparse and transcriptionally inactive compartment B at the megabase scale 6 , 11 . Topologically associating domains (TADs) are formed at the sub-megabase scale, which functions as units for gene regulation 28 . Chromatin loops facilitate long-range interactions between enhancers and promoters for gene regulation within TADs 20 . The 3D organization of the genome is dynamically regulated in key biological processes such as stem cell differentiation 29 , cell division 30 , and B-cell activation 31 . This study comprehensively analyzes Hi-C, genomic resequencing, and RNA-seq across five multiple myeloma (MM) and control plasma cells. Consistent with previous study 5 , we found that the 3D cancer genome is influenced by cancer-specific genome alterations and differential gene expression events. For the majority of these CNV related genes, a reduction in the number of detected interactions was observed. The level of interactions was found to be lower in multiple myeloma (MM) compared to the control groups. Many genes related to CNV were also found with loop difference, such as GPR18 and GPR183 . These two genes both showed CNV loss in MM patients. The GPR18 gene is related to the number of B cells and the expression of B cell-related genes 32 , which may have clinical value for the prognosis of various cancers, including multiple myeloma. A recent study indicates that GPR183 is highly expressed in cell lines resistant to HHT (an anti-tumor drug), suggesting that it may be associated with drug resistance in tumor cells 33 . At the compartment level, we found that more genes were activated, as there were 408 genes changed from compartment B state to A, and only 321 genes changed from compartment A to B. GO enrichment analysis (BP) of genes with A-to-B switching event showed that these inactive genes were involved in “homophilic cell adhesion via plasma membrane adhesion molecules ” term, which indicated that there was a decrease function for “Cell-cell adhesion via plasma-membrane” term. The genes in this term include PCDH9 , LRFN3 , IL1RAP , PCDHA6 , PCDHA9 , CDHA8 , PCDHA7 , PCDHA5 , PCDHA4 , PCDHA2 , PCDHA1 , PCDHA13 , PCDHA11 , PCDHA10 , PCDHA12 , and PCDHA3. A previous study investigated the interaction between B9/BM1 cells and osteoclasts and showed the possibility of tumor metastasis in bone marrow 34 . CML hematopoietic stem cells expressing IL1RAP can be targeted by chimeric antigen receptor-engineered T cells 35 , the inactivation of this gene might affect its function. At the TAD scale, consistent with the previous study 5 , we also found that MM genomes contain more TADs (~ 3627 in MM and 3197 in normal cells), and the average TAD size is smaller than in normal plasma cells (~ 0.70 Mb in MM and 0.72 Mb in normal cells). It is recommended that heterogeneity of cancer cells may contribute to more diverse 3D genomes within a cell population, increasing the detected TAD numbers 5 , 36 . These 43 genes were found in MM-specific TADs. The genes were mainly related to “Human immunodeficiency virus 1 infection” (including genes TNFRSF1B , FBXW11 , NFATC1 and AP1S3 ), “Wnt signaling pathway” (including genes FBXW11 , NFATC1 , RSPO3 , and FZD9 ), and “Cytokine − cytokine receptor interaction” (including genes TNFRSF1B , TNFRSF8 , IL19 , IL20 , and IL24 ) KEGG pathways. Receptor activator of nuclear factor (NF)-κΒ ligand stimulation in multiple myeloma-derived osteoclasts induced elevated NFATC1 , and selenoprotein W into the nucleus as compared to that in the control cells 37 . Our results showed that genes in the cytokine-cytokine receptor interaction KEGG pathway, including IL19 , IL20 , and IL24 changed their 3D structures, which could regulate the immune system and have inflammation effects 38 . Interleukin-20 (IL-20) is a pro-inflammatory cytokine with diverse angiogenic properties, serum IL-20 concentrations were found to participate actively in the pathophysiology of MM progression 39 . The presence of IL24 might affect tumor growth using a mouse model, but not as much as the therapeutic effect of HSV-tk, injection of IL24 reduced the tumor size 40 . At the loop level, these differentiated loops involved 113 genes, mainly in “AGE-RAGE signaling pathway in diabetic complications” and “Autophagy–animal” KEGG pathways. The genes were mainly MAPK10 , EGFLAM , COL4A5 , GAD1 , ARSB , MNX1 , GUCY2F , PABPC3 , CIR1 , CPLX1 , WDR36 , PITX2, NKX3-1 , GATA4 , and POM121L2 . The identified chromatin interactions and differential gene expression patterns provide insights into the molecular mechanisms of MM, highlighting pathways such as immune response and cell adhesion that could be targeted therapeutically. These findings suggest that drugs interfering with these pathways or modulating the expression of key genes involved in MM pathogenesis might serve as potential therapeutic targets. In addition, by integrating 3D genome data, we found that genes such as NSG2 , ALDH1A2 , and CALCRL showed a higher expression in the MM patients. NSG2 is a member of the neuron-specific gene (NSG) family, which is specifically expressed in neurons [17] and localized to the plasma membrane, the trans-Golgi network, and multiple endolysosomal compartments 41 . The aldehyde dehydrogenase 1 (ALDH1) family contains major enzymes that produce retinoic acid by the oxidation of all-trans-retinal and 9-cis-retinal, which mainly participates in biological functions such as cell differentiation, apoptosis, cell cycle arrest, and eventually 42 – 44 . A previous study showed that ALDH1A2 expression is regulated by the epigenetic regulation of DNMTs, and subsequently might act as a tumor suppressor in ovarian cancer 45 . CALCRL is a G protein-coupled receptor that regulates the concentration of calcium ions in cells 46 . It could inhibit cell proliferation and angiogenesis 47 . CALCRL also contributes to the drug resistance in AML by controlling the ADM-CALCRL axis 48 , 49 . ARHGAP24 showed a higher expression in the control. The Rho GTPase activating protein 24 (ARHGAP24) has been reported as a tumor suppressor in multiple cancers 50 . These results indicated that the NSG2 , ALDH1A2 , CALCRL , and ARHGAP24 genes might also have important potential functions during the multiple myeloma process. In addition, this study's findings may be limited by a relatively small sample size of multiple myeloma patients with the same symptom, age and sex, which could affect the generalizability of the results. Meanwhile, recent studies 51 , 52 have shown that single-cell RNA sequencing is a crucial technique for investigating cellular heterogeneity, highlighting the need for its incorporation in future research. Further studies should consider there are more than 2 MM patients and controls with the same sex, age and symptoms for analysis. Combining 3D genome, genome, and transcriptome analyses, we reveal that during MM development, multiple levels of alterations, such as spatial genome reorganization, occur accompanied by gene expression. The 3D genomic architecture identified in this study provides a foundation for exploring the spatial organization of genes and their regulatory elements in MM, which could lead to the development of targeted therapies that disrupt specific chromatin interactions crucial for the disease's progression. For instance, the identification of key genes such as NSG2 , ALDH1A2 , and CALCRL , which show differential expression in MM patients, suggests potential avenues for therapeutic intervention, possibly through small molecule inhibitors or gene editing technologies. Furthermore, understanding the 3D genome's role in antigen presentation, as indicated by the involvement of MHC class II protein complex assembly, could pave the way for novel immunotherapies that enhance the immune system's ability to recognize and attack myeloma cells. However, since cancer types are diverse and alterations are heterogeneous, the phenomena observed in one may not hold in other cancer types. In the future, we need to investigate whether these observations are universal phenomena across cancer types. Materials and methods Plasma Cell Identification In this study, MACSprep Multiple Myeloma CD138 MicroBeads (Order no. 130-111-744) have been developed to positively select CD138 + cells directly from whole blood. The operation method is modified according to the manufacturer's manual, and the details are described in the following process. Adjust Cell Concentration: Ensure the specimen is qualified, with no hemolysis or coagulation; Use an automated hematology analyzer to measure the white blood cell count and calculate the suitable cell number: Suitable Cell Number = White Blood Cell Concentration (WBC) × Specimen Volume; Calculate the volume of cells to be added per tube (µL) = 1×10 6 / Cell Concentration. Cell Surface Marker Staining: Label the tubes according to the experiment number and the antibody combination to be tested; Add the pre-prepared cocktail reagent to each tube for the chosen antibody combination; Mix the specimen well (at least 5 times by inversion), and add the calculated volume of cells to the bottom of the tube, mix well, and incubate in the dark for 15–20 minutes; Add 500µl of lysing solution, place in the dark for 10 minutes until complete hemolysis occurs; Centrifuge at 2000 rpm for 3 minutes, discard the supernatant; Add 2mL of 1% newborn calf serum in PBS buffer to resuspend the cells, mix well, centrifuge at 2000 rpm for 3 minutes, and discard the supernatant; Add an appropriate amount (about 200–600µL) of 1% paraformaldehyde fixative, resuspend the cells; Filter through a regular mesh with a pore size greater than 200 mesh, observe with the naked eye until no visible flocculent material or precipitate is present, and re-filter if necessary before preparing for machine detection. Intracellular Staining of Samples: Add 0.5 mL of 1× FACS permeabilizing solution, mix well, and incubate at room temperature in the dark for 5 minutes; Add 2 mL of PBS, mix well, centrifuge at 2000 rpm for 3 minutes, and discard the supernatant; Mix the cells, add the standard amount of fluorescent McAb against intracellular antigens, mix well, and incubate at room temperature in the dark for 30 minutes; Add 2mL of PBS, mix well, centrifuge at 2000 rpm for 3 minutes, and discard the supernatant; Add an appropriate amount (about 200–600µL) of 1% paraformaldehyde fixative, mix well, and store in the dark at 2–8°C, analyze within 24 hours; Filter through a regular mesh with a pore size greater than 200 mesh, observe with the naked eye until no visible flocculent material or precipitate is present, and re-filter if necessary before preparing for machine detection. Plasma Cell Enrichment: CD138 Magnetic Bead Method: Use 1-2mL of Running buffer to moisten the purple blood filter plug; Take 2-4ml of blood into the filtration set and mark the blood level in a 15mL tube; After centrifugation at 1400r/min for 10 minutes, discard the supernatant and part of the settled red blood cells; Add Running buffer up to the mark, then add 100–150µL of magnetic beads, mix slightly, and stand for 15 minutes; Use the magnetic bead column, first moisten with Running buffer, then filter the blood sample; Take Running buffer to the mark in a large tube, wash three times; Remove the magnetic bead column, add 5mL of Elution buffer, and quickly press down with a gas plug; Centrifuge at 1400r/min for 10 minutes and discard the supernatant. Fish Detection: the enriched plasma cells were exposed to 2×SSC, 2×SSC, 70% ethanol, 80% ethanol and 100% ethanol for 3min, and then dried. Adding Probes, Hybridizing: Denaturing at 88℃ for 2min, hybridizing at 45℃ for 2-16h; 2×SSC at room temperature for 1min and in 0.3%NP-40/0.4×SSC solution preheated at 68℃ for 2 min; Preheated distilled water at 37℃ for 1min and naturally dried in the dark; Add 10µL of hybrid blue staining solution to the target area of the slide, and cover the slide. Select the appropriate filter to observe under the fluorescence microscope. Plasma Cell Freezing and Thawing, Freezing: Place the collection tube containing plasma cells into liquid nitrogen for rapid freezing and store it in a -80°C refrigerator for future use. Thawing: Place the plasma cell freezing tube in the − 80°C refrigerator on ice. Continuously shake in a 37°C constant-temperature water bath until the cells are thawed and ready for use. Hi-C library construction, quality control and sequencing Cell cross-linking: the sample was fixed with formaldehyde, and the intracellular protein was cross-linked with DNA to preserve the interaction and maintain the 3D structure of the cell. Endonuclease digestion: DNA was digested by restriction endonuclease to produce sticky ends on both sides of the cross-linking. The restriction enzyme used in this project is DpnII. Terminal repair: Biotin-labeled bases are introduced to facilitate the purification and capture of subsequent DNA using the mechanism of terminal repair. Cyclization: cyclization of the DNA repaired at the end, and cyclization between the DNA fragments containing interactions, to ensure that the location of the interaction DNA is determined in the process of subsequent sequencing and analysis. DNA purification and capture: the DNA was de-crosslinked, and the purified DNA was broken into 300bp-700bp fragments. The DNA fragments containing the interaction were captured by streptavidin magnetic beads and the library was constructed. After the construction of the library, the concentration of the library and the size of the inserted fragment (Insert Size) were detected by Qubit 2.0 and Agilent 2100, respectively, and the effective concentration of the library was quantified accurately by the Q-PCR method to ensure the quality of the library. After passing the library inspection, high-throughput sequencing was carried out on the Illumina platform, and the sequencing read length was PE150. Raw data (raw reads) of fastq format were first processed through in-house Perl scripts. In this step, clean data were obtained by removing reads containing adapter, reads containing ploy-N, and low-quality reads from raw data. At the same time, Q20, Q30, and GC content of the clean data were calculated. All the downstream analyses were based on clean, high-quality data. Clean Reads used BWA (Burrows-Wheeler Aligner, v0.7.17) 53 to compare the two-terminal sequencing data with the reference genome sequences, respectively. The reads that can be compared are called Mapped Reads. Alignment efficiency refers to the percentage of Mapped Reads in Clean Reads. Using BWA 53 with the command “mem -t 10-k 32,” to align the two-terminal sequencing data with the assembled genome sequence to obtain the only Read. Then using HiC-Pro (An optimized and flexible pipeline for Hi-C data processing, v2.10.0) 54 to analyze the alignment results, identify the Valid Interaction Pairs and Invalid Interaction Pairs. We used HiC-Pro v2.10.0 54 to obtain the corresponding standardized interaction matrix at various resolutions (10-, 20-, 100-kb, and 500-kb), and then calculate the Pearson correlation coefficient among the five samples. Resolution analysis Sequencing depth of data determines the resolution of Hi-C data (the size of bin). Different resolutions should be used to study different sequencing depths of data. The resolution is calculated based on the method in reference 20 . That is, the number of interactive reads between each bin is arranged from high to low according to the different sizes of the bin. When 80% of the bin is arranged, the bin still covers the size of the smallest bin with more than 1000 reads, satisfying this condition, that is, the resolution of the Hi-C library. The Hi-C data are standardized by using HiC-Pro v2.10.0 54 software. Then the relationship between interaction frequency and genome linear distance is calculated by HiCdat 55 . The slope of the model is the corresponding IDEs (Interaction decay exponents). In the measured samples, it can be observed that the interactive signal decreases rapidly with the increase in distance. The mds algorithm of Pastis software is used to simulate the three-dimensional location of chromatin 56 . The software generates pdb (protein data bank) files based on Poisson distribution and uses pymol software to visualize the three-dimensional structure. The PCA (principal component analysis) was calculated by HiTC v1.24.0 software 57 with a bin size of 100 Kb. The PCA value was positive for the A compartment with high gene density and negative for the B compartment with low gene density. If there is a biological repetition for each sample, we merge the data from each library and identify A and B. TAD and loop analysis The identification of topology-related domains (TAD) was carried out according to bin = 40 Kb using TadLib 58 software. At the same time, the software HOMER (Hypergeometric Optimization of Motif EnRichment, v5.1) 59 was used to calculate the ratio of IR (Inclusion Ratio), retain the TAD with IR > 1, and filter out the TAD with a length of less than 5 bins to obtain the final TAD. The DI values of each sample were calculated. Finally, the DI delta value of each TAD is further calculated (that is, the average difference of DI of 4 bins upstream and downstream of the TAD boundary). Then, the limma package was used to calculate the difference between the control and MM samples. Only when the FDR value of difference significance was less than 0.01, and the DI delta score of the two groups of samples was not all greater than 200 could it be considered as a TAD boundary of difference. Loop identification was using Juicer (v2.0) 60 for analysis with bin size = 25 Kb, FDR ≤ 0.01. To compare the loop structures between different samples, we first merge the loops from each sample 61 , remove redundancy from the merged loops of all samples, and then calculate the original interaction values for each sample's non-redundant loops as input for edgeR (v3.8.6) 62 . Loops with an FDR value less than 0.01, a significant P value less than 0.05, and an FC (Fold Change) greater than 1.5 are considered as differential loops. Gene ontology analysis The gene ontology (GO) enrichment analysis was carried out by using clusterProfiler R software package, and the hypergeometric test was used to find the GO items which were significantly enriched compared with the whole genome background. KEGG (Kyoto Encyclopedia of Genes and Genomes) 63 , 64 pathway enrichment analysis was also conducted. We used KOBAS (KEGG Orthology Based Annotation System, v3.0) 65 software to test the statistical enrichment of differential expression genes in KEGG pathways. We used clusterProfiler R packages to find KEGG pathways that are significantly enriched compared to the entire genome background. Whole-genome sequencing experiments and analysis The whole genome DNA of the five MM patients was extracted and sequenced at an average 37.5× depth through XTen (Illumina). The sequenced reads were mapped to the human reference genome (hg19) by the bwa-mem software 66 . Only uniquely mapped reads were used for downstream analysis. The Picard software ( http://broadinstitute.github.io/picard ) was used to remove PCR duplicates. The total mapping rate is above 90% for each sample. The detection of SNP (Single Nucleotide Polymorphism) and small INDEL (small Insertion and Deletion) is mainly implemented by GATK software (The Genome Analysis Toolkit, v4.0) 67 . We next used Manta (v1.6.0) 68 to identify genomic structural variation (SV). RNA-seq experiments and analysis We use the TRIzol Reagent (Life Technologies, California, USA) instruction manual to extract total RNA from cells. Use the NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE) to measure the concentration and purity of RNA. Evaluate the integrity of the RNA using the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA) with the RNA Nano 6000 Kit. The RIN (RNA integrity number) values ranged from 8.0 to 9.4. Indicating that all sample’s RNA were qualified, the quality meets the requirements of database construction, the total amount meets two or more conventional quantitative databases. Magnetic beads coated with oligo(dT) are used to selectively capture mRNA from total RNA. First-strand cDNA synthesis is then performed, followed by the synthesis of the second strand of cDNA. The overhangs at the ends of the cDNA are repaired to create blunt ends through the activity of exonucleases and polymerases. After the 3' ends of the DNA fragments are adenylated, NEBNext adapters with a hairpin loop structure are ligated to them. The library fragments are then purified using AMPure XP magnetic beads from Beckman Coulter in Beverly, USA. Subsequently, 3µl of USER Enzyme from NEB (USA) is added, and the mixture is incubated at 37°C for 15 minutes. Before proceeding to PCR, the reaction is heated at 95°C for 5 minutes to denature any remaining secondary structures. The PCR is then carried out using a high-fidelity DNA polymerase, along with universal PCR primers and index (X) primers. Finally, the PCR products are purified once more using AMPure XP magnetic beads, and the quality of the library is assessed using an Agilent Bioanalyzer 2100 system. The sequenced reads were mapped to the human reference genome (hg19) by TopHat (v2.1.1) 69 and gene expressions were quantified by Cufflinks (v2.2.1) 70 . We used the RStudio software for the downstream statistical analyses. PUREE 23 was used to test the tumor purity of these MM samples. Conclusion In summary, we have investigated the 3D genome of MM and analyzed the differences at compartment, TADs, loops, genomes, and gene expression levels to identify MM-related pathways and key genes. These findings extend our understanding of MM, which may implicate in clinical treatment and drug development for MM. Declarations Ethics statement All methods carried out in accordance with the relevant guidelines and regulations. This study has been approved by the Ethics Committee of Chengdu First People's Hospital, ethics number ZXKY No.002, in 2020. All patients and blood donors in this study have signed informed consent forms by themselves or their guardian(s). Funding: This work was supported by The Science and Technology Department of Sichuan Province (No.2020YJ0438). Conflict of interest statement : The authors declare no conflicts of interest regarding this work. Author Contribution Conceptualization, K.Z.,Y.L., and X.F.; writing—original draft preparation, K.Z., M.C.(Mengsi Chen) ,M.C.(Ming Chen) ,Y.W. and H.L.; writing—review and editing, Y.L., X.G., L.L.,L.T.,X.L., D.H., and X.F.; Supervision, X.L., D.H., and X.F.; funding acquisition, K.Z.; All authors have read and agreed to the published version of the manuscript. 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medicine，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Mengsi","middleName":"","lastName":"Chen","suffix":""},{"id":440116852,"identity":"93f33eb2-ec5f-4748-84d3-840e9bb00cc6","order_by":2,"name":"Ming Chen","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese medicine，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Chen","suffix":""},{"id":440116853,"identity":"1cc2f3d5-1e73-416b-a26f-0c2a20b5e802","order_by":3,"name":"Yue Wang","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese medicine，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wang","suffix":""},{"id":440116854,"identity":"d83299de-266f-47a9-9974-436d889710d8","order_by":4,"name":"Haibo Liu","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese medicine，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Liu","suffix":""},{"id":440116855,"identity":"3c653210-007f-4f1f-808f-a7c9938f35dc","order_by":5,"name":"Yanju Li","email":"","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University，Guiyang","correspondingAuthor":false,"prefix":"","firstName":"Yanju","middleName":"","lastName":"Li","suffix":""},{"id":440116856,"identity":"a8a80ee1-9f62-49e2-9b6c-f95323f1131a","order_by":6,"name":"Xiaohong Guan","email":"","orcid":"","institution":"Chengdu First People's Hospital，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Guan","suffix":""},{"id":440116857,"identity":"5635dd3d-475c-4ca8-a75b-8145551d4baa","order_by":7,"name":"Lihua Lei","email":"","orcid":"","institution":"Chengdu First People's Hospital，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Lei","suffix":""},{"id":440116858,"identity":"8ee2802e-2247-444e-8e7b-c0db131ad73f","order_by":8,"name":"Li Tao","email":"","orcid":"","institution":"Chengdu First People's Hospital，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Tao","suffix":""},{"id":440116859,"identity":"090b070e-0165-4952-ae2c-cdd7a99455fc","order_by":9,"name":"Xiaoxiao Liu","email":"","orcid":"","institution":"Chengdu First People's Hospital，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxiao","middleName":"","lastName":"Liu","suffix":""},{"id":440116860,"identity":"91c1968a-2e03-42f4-a2a5-043176d5a7cf","order_by":10,"name":"Dong He","email":"","orcid":"","institution":"Chengdu First People's Hospital，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"He","suffix":""},{"id":440116861,"identity":"21dcc491-3b83-4404-9447-ba2f54ff9b7e","order_by":11,"name":"Xiaoli Fei","email":"","orcid":"","institution":"Chengdu First People's Hospital，Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Fei","suffix":""}],"badges":[],"createdAt":"2024-12-17 15:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5663072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5663072/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-03132-2","type":"published","date":"2025-06-02T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80281914,"identity":"a4dedec4-245c-45c3-b7b5-1f2d3592d501","added_by":"auto","created_at":"2025-04-10 06:06:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2446447,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of 3D organization in MM plasma cells. \u003cstrong\u003e(A)\u003c/strong\u003e Genome-wide contact maps of MMC1 patients. \u003cstrong\u003e(B)\u003c/strong\u003e P(s) curves (at 100 kb resolution) averaged across all autosomes in the genome of each sample.\u003cstrong\u003e (C)\u003c/strong\u003e Compartment state of chromosome 1 indicated by PC1 values of MMC1 patients. Red represents A compartment and cyan represents B compartment. \u003cstrong\u003e(D) \u003c/strong\u003eCompared with Control, the MM patients showed a few compartment changes in common. \u003cstrong\u003e(E) \u003c/strong\u003eThe most enriched terms for genes with A to B or B to A switching. F. Enrichment analysis of genes with A-to-B switching event.\u003c/p\u003e","description":"","filename":"Figure100.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5663072/v1/f0e978956a67e77a11a2028b.jpeg"},{"id":80280548,"identity":"c53a3279-137e-4412-b8ec-bb8f4cb31e0a","added_by":"auto","created_at":"2025-04-10 05:42:39","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":433081,"visible":true,"origin":"","legend":"\u003cp\u003eTAD and loop characteristics. \u003cstrong\u003e(A)\u003c/strong\u003eNumber of TADs in each sample.\u003cstrong\u003e (B)\u003c/strong\u003eBoxplots showing TAD sizes in each sample.\u003cstrong\u003e (C-D) \u003c/strong\u003eGO enrichment biological process (BP) \u003cstrong\u003e(C)\u003c/strong\u003e and KEGG \u003cstrong\u003e(D)\u003c/strong\u003e analysis of genes with TAD boundary of difference. \u003cstrong\u003e(E) \u003c/strong\u003eHi-C contact matrix and TADs on Chr 1: 1–10Mb in control and MMC1 patient.\u003c/p\u003e","description":"","filename":"Figure200.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5663072/v1/960f001191db9910cd72d2d2.jpeg"},{"id":80281919,"identity":"4a3d88ae-e506-4f51-b915-8ae128dd6488","added_by":"auto","created_at":"2025-04-10 06:06:47","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1028134,"visible":true,"origin":"","legend":"\u003cp\u003eLoop characteristics in MM.\u003cstrong\u003e (A) \u003c/strong\u003eThe identified Loop is classified into three types. E-E, E-P, and P-P are the number of enhancer-enhancer interaction, enhancer-promoter interaction, and promoter-promoter interaction loops, respectively. Loop classification according to the number of enhancers\u003cstrong\u003e (B)\u003c/strong\u003e and promoters\u003cstrong\u003e (C)\u003c/strong\u003e of each loop contained.\u003cstrong\u003e (D) \u003c/strong\u003eDistribution of chromatin loop in each chromosome in control (red) and MM (green) groups. GO enrichment biological process (BP) \u003cstrong\u003e(E)\u003c/strong\u003e and KEGG\u003cstrong\u003e (F)\u003c/strong\u003e analysis of genes with loops of difference.\u003c/p\u003e","description":"","filename":"Figure300.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5663072/v1/fef30e2b24b19894498fb127.jpeg"},{"id":80281916,"identity":"58fffb57-7784-431f-8872-5e243d0d8d41","added_by":"auto","created_at":"2025-04-10 06:06:46","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":431293,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic variations of MM. \u003cstrong\u003e(A) \u003c/strong\u003eDistribution of variations on the genome. From outside to inside were chromosome position, gene density, SNP density and InDel density respectively.\u003cstrong\u003e (B)\u003c/strong\u003eDistribution of SNPs of each type based on all samples. \u003cstrong\u003e(C)\u003c/strong\u003eVeen diagram showing number of genes with variations (including Non-synonymous SNP, InDel and SV) shared among the five MM patients. KEGG\u003cstrong\u003e (D)\u003c/strong\u003e and Reactome\u003cstrong\u003e (E) \u003c/strong\u003eenrichment analysis of the shared genes with variations in the CDS region.\u003c/p\u003e","description":"","filename":"Figure400.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5663072/v1/e05c7d36057df143acb38cea.jpeg"},{"id":80279426,"identity":"b074b4bc-f696-46f5-b8a6-97a92a312020","added_by":"auto","created_at":"2025-04-10 05:34:40","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":485123,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis on DEGs. (\u003cstrong\u003eA)\u003c/strong\u003eFPKM density distribution of each sample.\u003cstrong\u003e (B)\u003c/strong\u003eCorrelation heatmap between samples. (\u003cstrong\u003eC)\u003c/strong\u003eMA plot of differentially expressed genes between MM patients and Control.\u003cstrong\u003e (D)\u003c/strong\u003eHierarchical clustering of differentially expressed genes. (\u003cstrong\u003eE)\u003c/strong\u003eBubble chart of KEGG pathway enrichment on DEGs. Top 20 enriched pathways (with smallest Q-value) were shown.\u003c/p\u003e","description":"","filename":"Figure500.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5663072/v1/a147e39ba083f53cc9015446.jpeg"},{"id":80281913,"identity":"5128b281-e6ed-4bbf-83d3-a0d95118ef78","added_by":"auto","created_at":"2025-04-10 06:06:40","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":455097,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-omics analysis. \u003cstrong\u003e(A)\u003c/strong\u003e Venn diagram showing the overlapped genes for differentiated compartment change, TADs and loops.\u003cstrong\u003e (B)\u003c/strong\u003e Venn diagram showing overlap between genes with CDS variation and DEGs.\u003cstrong\u003e (C)\u003c/strong\u003eEnrichment analysis on overlapping genes between genes with CDS variation and DEGs. \u003cstrong\u003e(D) \u003c/strong\u003eFPKM of \u003cem\u003eARHGAP24\u003c/em\u003e, \u003cem\u003eALDH1A2\u003c/em\u003e,\u003cem\u003e CALCRL\u003c/em\u003e, and\u003cem\u003eNSG2 \u003c/em\u003egenes in four MM patients and Control.\u003c/p\u003e","description":"","filename":"Figure600.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5663072/v1/96e2d2a1e2d9113f71736968.jpeg"},{"id":84242884,"identity":"751e55fe-4871-47b1-9960-16bc980e7c51","added_by":"auto","created_at":"2025-06-09 16:12:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6373091,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5663072/v1/9aa294a1-4cd2-45f8-9a22-0d52351e1777.pdf"},{"id":80279404,"identity":"8aeab9c1-e1dc-46ed-84ae-a27307368b61","added_by":"auto","created_at":"2025-04-10 05:34:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":572040,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5663072/v1/55d8eb6eae425ae01134b6d3.pdf"},{"id":80280551,"identity":"aca3ee42-117c-44d4-980b-3106045a1299","added_by":"auto","created_at":"2025-04-10 05:42:39","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":173943,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5663072/v1/1b1ad19d050723cedf5a3cff.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The 3D genome of plasma cells in multiple myeloma","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMultiple myeloma (MM) is a form of cancer that predominantly affects adults and is centered around the excessive multiplication of plasma cells within the bone marrow \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is an incurable disease, and most patients experience relapse/refractory \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, posing significant challenges for patients and healthcare providers. However, targeted immunotherapeutic strategies such as CAR-T, CAR-natural killer (NK), and TCR-T have emerged as a beacon of hope in the treatment of MM due to their potential to minimize harm to healthy tissues \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent applications of chromosome conformation capture techniques, particularly Hi-C, have revolutionized our understanding of the 3D cancer genome \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Studies using Hi-C data from different species have shown that the switching of chromatin compartments between active (A) and inactive (B) states is closely linked to changes in gene expression \u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In prostate cancer, topologically associating domains (TADs) are reduced in size and exhibit alterations at key tumor suppressor loci, such as \u003cem\u003eTP53\u003c/em\u003e \u003csup\u003e9\u003c/sup\u003e. Previous studies have shown that the destruction of TAD boundaries will lead to the regulation of the genes and the occurrence of diseases \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Therefore, the TAD boundary plays a very important role. Deleting the boundary section will cause a disorder of gene regulation, resulting in the transcription of originally silenced genes, while those that should have been transcribed were silenced \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The boundaries of TAD can not only guide the folding of chromosomes into higher structures, but also correctly guide the remote transcriptional regulation \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Moreover, the spatial architecture of the genome actively shapes the nature of cancer-associated genomic alterations \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These insights reveal a bidirectional relationship between the 3D organization of the genome and the genetic changes that drive cancer \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Understanding this interplay is crucial for unraveling the molecular mechanisms of cancer and for developing targeted therapies that consider the spatial context of genomic alterations \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. A previous study found that in the context of multiple myeloma (MM), the three-dimensional (3D) organization of the genome undergoes significant changes when compared to normal B cells \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These alterations are characterized by an increase in the number of topologically associating domains (TADs), a reduction in the average size of these domains, and a shift in the chromatin state of certain genomic regions \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Multi-omics analysis is an important method to explore the formation of traits and the development of diseases \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, limited multiple myeloma study was explored using 3D genomics methods based on the different patients.\u003c/p\u003e \u003cp\u003eDespite considerable progress, the development of extensive 3D datasets that capture the cancer genome's structure is still in its infancy. Therefore, in-depth research into the disease's molecular mechanisms and therapeutic targets is crucial. Plasma cells are a specialized type of white blood cell that plays a vital role in the immune system. They are derived from B lymphocytes, a type of immune cell that produces antibodies. 3D genomics is an emerging field of study that focuses on the spatial organization of the genome. However, the 3D genomic study of plasma cells is limited. Characterizing the cancer genome's spatial disarray and its functional implications is paramount, especially considering the prevalence of genomic changes such as mutations in cancer. In our study aimed at unraveling the molecular characteristics of MM cancers, we employed a multi-faceted analysis. We utilized Hi-C contact maps, WGS, and RNA-seq data from 5 patients and 1 control normal person (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) to scrutinize potential biases in Hi-C data. Our findings unveiled a significant link between the architectural features of the 3D genome, genomic variations, gene expression, and MM cancer. This integrated analysis enhances our comprehension of the 3D cancer genome's role in MM and opens new avenues for therapeutic intervention by targeting genomic disarray.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical information about the patients\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe cohort of this study consists of individuals aged between 44 and 80, with both male and female participants (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The group includes five MM patients, each with distinct treatment regimens. The treatments range from combinations of bortezomib, thalidomide, and dexamethasone to more complex therapies involving pomalidomide, bortezomib, dexamethasone, daratumumab, and selinexor. The time of diagnosis varies, with the earliest in August 2018 and the latest in July 2023. Genetically, the patients exhibit a range of chromosomal abnormalities. For instance, several individuals have FISH results showing 1q21 amplifications and deletions of 13/13q. One patient has a complex karyotype with multiple abnormalities, including deletions, translocations, and aneuploidy, while another shows a highly abnormal karyotype with numerous structural and numerical changes. In contrast, one patient has normal FISH and karyotype results. Overall, this group is characterized by significant genetic heterogeneity and diverse treatment approaches, reflecting the complexity of their conditions. In addition, all MM patients were seriously ill and died within 2 years after diagnosis, these samples had certain characteristics and were difficult to collect. With this clinical context in mind, we next explored the 3D genomic architecture of MM plasma cells to understand how chromatin organization might contribute to disease progression.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDynamic changes in compartmentalization and local accessibility\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo elucidate the multiscale rewiring of chromatin architecture and its influence on MM plasma cells, we used in situ Hi-C to map chromatin contacts for plasma cells of five patients (MMC1, MMC2, MMC3, MMC5, and MMC6) and one control sample (Control). As a result, the average clean data of the sample is 180.37 Gb. We then generated a total of 1.45\u0026nbsp;billion valid contacts (with an average of 242.28\u0026nbsp;million contacts per sample (\u003cb\u003eTables S2\u003c/b\u003e) and reached a maximum resolution of 8.15 kb (\u003cb\u003eTables S2\u003c/b\u003e). Most (57.18%) contacts occurred within chromosomes and consisted of the dominant (87.61%) long-range interactions (\u003cb\u003eTables S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe then constructed genome-wide contact maps by dividing the genome into 500 kb regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e). Inter‑chromosomal interactome indicated that the chromosomes with similar lengths have a similar likelihood to mutually contact each other, which was consistent with the previous study \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. All samples showed a strong decrease in contact probability with an increase in the distance between loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Compartment A, rich in actively transcribed genes, features open chromatin with histone marks for active transcription and is centrally located within the nucleus. In contrast, Compartment B, with fewer genes and inactive transcription, has closed chromatin with marks for gene silencing and is peripherally situated in the nucleus \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These compartments are crucial for genome organization and gene regulation \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. We identified the compartment state of each sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and identified substantial levels of compartmental switching in plasma cells across control and 5 MM patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In these regions, 408 bins were switching from B to A (which contained 259 genes) and 321 bins from A to B (contained 274 genes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis demonstrated that genes embedded in regions experience the A-to-B switching event and were primarily involved in signaling receptor activity and G protein-coupled receptor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). This includes molecular transducer activity, transmembrane signaling receptor activity, interleukin-1receptor activity and detection of chemical stimulus involved in sensory perception (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Nonetheless, genes located in regions that were subject to B-to-A switching events were primarily involved in DNA-binding transcription factor activity and RNA polymerase II transcription regulatory region sequence-specific DNA binding molecular function, it is related to the development of organs and system of animals. In addition, it is also enriched in type III interferon signaling (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMost TADs were highly stable\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHaving established compartment-level changes in chromatin organization, we next examined the stability and alterations of topologically associating domains (TADs) in MM plasma cells to identify specific regulatory units associated with disease development. At the sub-megabase scale, the local chromatin architecture can be characterized by TAD \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. A topologically associating domain (TAD) is a highly self-correlated continuous region where the interactions between fragments tend to be more within the TAD than between TADs, and it is separated from its neighbors by distinct boundaries to form an independent regulatory unit that presents a square structure on the heat map diagonal. As a regulatory unit, genes in TAD share common regulatory elements, so there are cooperative expression characteristics of genes in TAD (providing basis for co-expression of adjacent genes on chromosomes). According to the Inclusion ratio (IR), we identified 3197 to 3988 TADs in the six samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), with an average of 702.45 Kb length (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cb\u003eTables S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe next calculated the genome-wide DI (Directionality index score) values of the samples to explore the differential TADs among all samples. Then, the TAD boundaries of all samples are merged. As a result, compared with the control, we found 19 specific TAD boundaries (which embedded 43 genes) in the MM samples. These genes were mainly involved in \u0026ldquo;immune response\u0026rdquo;, \u0026ldquo;neutrophil degranulation\u0026rdquo;, \u0026ldquo;leukocyte degranulation\u0026rdquo;, \u0026ldquo;leukocyte activation\u0026rdquo; and \u0026ldquo;immune system process\u0026rdquo; biological process terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC); \u0026ldquo;Human immunodeficiency virus 1 infection\u0026rdquo;, \u0026ldquo;Wnt signaling pathway\u0026rdquo;, and \u0026ldquo;Cytokine\u0026thinsp;\u0026minus;\u0026thinsp;cytokine receptor interaction\u0026rdquo; KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD); \u0026ldquo;molecular function regulator\u0026rdquo;, \u0026ldquo;catalytic activity, acting on a protein\u0026rdquo;, and \u0026ldquo;guanyl\u0026thinsp;\u0026minus;\u0026thinsp;nucleotide exchange factor activity\u0026rdquo; molecular function terms (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u003c/b\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE showed an example fragment of 10Mb on chromosome 1 with resolution of 40Kb. These results indicate altered immune response due to formation of new and disappearance of the original TAD boundaries associated with multiple myeloma development. Building on the TAD analysis, we next explored chromatin loop structures to understand how promoter-enhancer interactions might contribute to gene expression changes in MM.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGlobal rewiring of loops in multiple myeloma\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIf the interaction frequency of a pair of chromosomal sites is higher than the interaction frequency of the adjacent chromosome segments on the linear line, then the pair is called a significant interaction site (which is also called loop) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, that shows the presence of strong interaction signal points at non-diagonal locations on the heat map. The existence of loop structure is the biological reason for the emergence of significant interaction sites, so we can identify significant interaction sites and identify loop structure through Hi-C data. The ends of the loop are referred to as the anchor points of the loop, which include common promoter-enhancer interaction (PEI) sites. We next identified significant interacting sites through Hi-C data and identifying loop structures. As a result, we identified 1069 loops from MMC6 to 6929 loops in control (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). If a loop where one anchor is in the promoter region (the 2 kb upstream of the gene transcription start site serves as the promoter region), and the other anchor is located in a non-promoter region (potential enhancer-like region) is referred to as a promoter-enhancer associated loop (PEL). Finally, we identified the greatest number of loop types was promoter-enhancer associated loops. Most loops were not anchored enhancers or promoters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C), and the number of each loop type shows a significant difference; given that patients MMC1 and MMC2 (normal karyotypes) have different karyotypes compared to patients MCC4, MMC5, MMC6 (complex karyotypes, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), it is presumed that MM patients with complex genomic variants lose more regio-interactions in the genome.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe differential loop analysis showed 10 specific loops in the 5 MM samples compared with the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These differential loop-related genes (if a bin size region upstream and downstream of the differentially loop-related boundary intersects with the promoter region of the gene) were further used to perform GO/KEGG enrichment analysis. These genes were mainly involved in \u0026ldquo;anterior/posterior pattern specification\u0026rdquo; biological process terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE); \u0026ldquo;Epstein\u0026thinsp;\u0026minus;\u0026thinsp;Barr virus infection\u0026rdquo;, \u0026ldquo;Autophagy \u0026ndash; animal\u0026rdquo;, \u0026ldquo;Relaxin signaling pathway\u0026rdquo;, and \u0026ldquo;ECM\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction\u0026rdquo; KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF); \u0026ldquo;regulatory region nucleic acid binding\u0026rdquo;, and \u0026ldquo;chromatin DNA binding\u0026rdquo; molecular function terms (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB\u003c/b\u003e). These findings indicate the potential role of chromatin loops in autophagy events in MM patients.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eGenomic characteristics of the MM\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWith insights from chromatin loops, we then turned to the genomic characteristics of MM to identify specific mutations and structural variations that might drive disease progression. To explore the genomic characteristics of MM patients, we determined the whole genomes of 5 patients, compared them with one control person, and identified the SNPs and other variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). As a result, we totally identified 6.58 M, 4.40 M, and 2.18 M SNPs, transitions, and transversions, respectively (\u003cb\u003eTables S3\u003c/b\u003e). In total, there\u0026rsquo;re 3,636,606 SNPs were identified in the intergenic region (55.28%), and only 45,438 SNPs (0.69%) were identified in CDS regions (\u003cb\u003eTable S4\u003c/b\u003e). Of these SNPs in CDS region, most of them were nonsynonymous (49.62%) and synonymous coding SNPs (49.23%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Similar to SNPs, small InDel annotation results also showed that most InDels were in intergenic regions (51.17%) (\u003cb\u003eTable S5\u003c/b\u003e). We also detected the structure variation (SV) of the MM samples. The greatest number of SV (69,169) was found in MMC2 (\u003cb\u003eTable S6\u003c/b\u003e), most of them were inversion (65,308). Copy number variation (CNV) detection results showed that the control has the least number of CNV (1067). These variations in the CDS region might cause the function change of the genes, compared with the reference genome, we found there were 6752, 927, and 1211 genes with non-synonymous SNP, InDel, and SV, respectively, for MMC1 patients (\u003cb\u003eTable S7\u003c/b\u003e). There are 7765, 12,497, 7957, 8266, and 8034 genes with variations (including Non-synonymous SNP, InDel and SV) in MMC1, MMC2, MMC4, MMC5, and MMC6 respectively. Among these genes, 4691 of them were shared among these five MM patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThese overlapped variated genes were further used to perform GO/KEGG enrichment analysis. These genes were mainly involved in \u0026ldquo;multicellular organismal process\u0026rdquo;, \u0026ldquo;response to stimulus\u0026rdquo;, and \u0026ldquo;developmental process\u0026rdquo; biological process terms (\u003cb\u003eFigure S3A\u003c/b\u003e); \u0026ldquo;ECM proteoglycans\u0026rdquo;, \u0026ldquo;Collagen formation\u0026rdquo;, \u0026ldquo;Collagen chain trimerization\u0026rdquo;, and \u0026ldquo;Diseases of glycosylation\u0026rdquo; Reactome terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD); \u0026ldquo;ECM-receptor interaction\u0026rdquo;, \u0026ldquo;Olfactory transduction\u0026rdquo;, \u0026ldquo;Graft-versus-host disease\u0026rdquo;, and \u0026ldquo;Taste transduction\u0026rdquo; KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE); \u0026ldquo;small molecule binding\u0026rdquo;, \u0026ldquo;ion binding\u0026rdquo;, and \u0026ldquo;protein binding\u0026rdquo; molecular function terms (\u003cb\u003eFigure S3B\u003c/b\u003e). These results implicate SNPs, InDels and SVs in the development of MM were associated with binding processes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome changes in MM plasma cells\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo further link genomic variations to gene expression changes, we performed transcriptome analysis to identify differentially expressed genes and pathways in MM plasma cells. We sequenced five samples (including Control, MMC1, MMC2, MMC4 and MMC6). RNA-seq results showed that after sequencing quality control, a total of 34.01 Gb of clean data was obtained (\u003cb\u003eTable S8\u003c/b\u003e). According to the expression of the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), the correlation analysis showed that these MM patients showed more similar expression pattern compared with control (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Compared with control, we identified 1,619 (1,209 up and 410 down regulated genes) differentially expressed genes (DEGs) in the four MM patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). These DEGs were mainly involved in \u0026ldquo;p53 signaling pathway\u0026rdquo;, \u0026ldquo;Various types of N\u0026thinsp;\u0026minus;\u0026thinsp;glycan biosynthesis\u0026rdquo;, \u0026ldquo;Pathways in cancer\u0026rdquo;, \u0026ldquo;Cell adhesion molecules\u0026rdquo; and \u0026ldquo;N\u0026thinsp;\u0026minus;\u0026thinsp;Glycan biosynthesis\u0026rdquo; KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE); \u0026ldquo;protein disulfide isomerase activity\u0026rdquo;, \u0026ldquo;ADP binding\u0026rdquo; and \u0026ldquo;AMP binding\u0026rdquo; GO molecular function terms (\u003cb\u003eFigure S4A\u003c/b\u003e); \u0026ldquo;negative regulation of cell adhesion\u0026rdquo;, \u0026ldquo;negative regulation of cell communication\u0026rdquo;, and \u0026ldquo;negative regulation of signaling\u0026rdquo; GO biological process terms (\u003cb\u003eFigure S4B\u003c/b\u003e). These results indicate a large proportion of perturbations in the transcriptome of MM blood with the majority of dysregulated protein-coding genes associated with adenylate binding.\u003c/p\u003e \u003cp\u003eTumors are intricate conglomerates of both malignant and non-malignant cells. The tumor purity\u0026mdash;defined as the proportion of cancer cells within a sample\u0026mdash;can complicate integrative analyses by introducing variability \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Conversely, it also presents an opportunity to study tumor heterogeneity, offering insights into the complex interplay between cancer cells and their surrounding microenvironment \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. We then used PUREE \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e to calculate the tumor purity of these MM samples. As a result, the MM samples had tumor purity values ranging from 0.46 (MMC1) to 0.58 (MMC4), which belonged to mid-high (0.38\u0026ndash;0.97) purity range samples \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The mixture of the genomes of tumor cells and normal cells, which may be responsible for the large number of mutations in the genome.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMulti-omics analysis\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIntegrating the multi-omics data, we explored the overlap between genomic variations, chromatin organization, and gene expression to identify key genes and pathways that may drive MM development. We first checked the overlapped genes among differentiated compartments, TAD, and loop (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Three hundred and fifty overlapped genes were found in DEGs and genomic CDS variated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These genes were mainly involved in \u0026ldquo;MHC class II protein complex assembly\u0026rdquo; and \u0026ldquo;Antigen processing and presentation of exogenous peptide antigen\u0026rdquo; GO biological process terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC); \u0026ldquo;fibronectin binding\u0026rdquo;, and \u0026ldquo;MHC class II protein complex binding\u0026rdquo; GO molecular function terms; and \u0026ldquo;Autoimmune thyroid disease\u0026rdquo;, \u0026ldquo;Cell adhesion molecules\u0026rdquo;, \u0026ldquo;N-Glycan biosynthesis\u0026rdquo;, \u0026ldquo;Intestinal immune network for IgA production\u0026rdquo; and \u0026ldquo;Hematopoietic cell lineage\u0026rdquo; KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In addition, we found that genes such as \u003cem\u003eNSG2\u003c/em\u003e, \u003cem\u003eALDH1A2\u003c/em\u003e, and \u003cem\u003eCALCRL\u003c/em\u003e showed a higher expression in the MM patients. \u003cem\u003eARHGAP24\u003c/em\u003e showed a higher expression in the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). NSG2 is a member of the neuron-specific gene (NSG) family, which is specifically expressed in neurons \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCytogenetic analysis has unveiled a complex landscape of genetic mutations in multiple myeloma (MM), with the majority of these alterations being concentrated in structural rearrangements and copy number variations (CNVs) \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Among these, the most commonly observed CNVs include gains of chromosome 1 and loss of chromosome 17 \u003csup\u003e26,27\u003c/sup\u003e. Here, consistent with previous studies, we also found that the most commonly observed CNVs include gains of chromosome 1 and losses of chromosome 13 were detected (\u003cb\u003eTable S9\u003c/b\u003e). We also found that CNV had an important effect on the loops difference in MM patients. Among the 161 loops differentiated between control and MM patients, 141 of them were related to genes (39% of these loops were related to CNV genes). These genes include \u003cem\u003eLRRC63\u003c/em\u003e, \u003cem\u003eGPR183\u003c/em\u003e, \u003cem\u003eOBI1\u003c/em\u003e, \u003cem\u003ePOU4F1\u003c/em\u003e, \u003cem\u003eZIC5\u003c/em\u003e, \u003cem\u003eCOG3\u003c/em\u003e, \u003cem\u003eUBAC2\u003c/em\u003e, \u003cem\u003eTM9SF2\u003c/em\u003e, \u003cem\u003eGPR18\u003c/em\u003e, and \u003cem\u003eZIC2\u003c/em\u003e. For most of these genes, decreasing number of interactions were detected, the interactions were lower in MM than the control, with an average \u0026minus;\u0026thinsp;5.73 of log2FC.\u003c/p\u003e \u003cp\u003eThese results indicate the functional relevance of each level of the 3D genome hierarchy (such as compartment, TAD and loop), and the variations of genome and establishment of 3D genome provides multiple regulatory layers to alter gene expression in MM development.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eChromatin conformation capture techniques, such as 3C, 4C, 5C, Hi-C, and ChIA-PET, have recently been developed to explore the three-dimensional (3D) genome organization of genomes at high-resolution \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and reveal gene regulation mechanisms \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Using these techniques, many studies have found that the mammalian and bird' genomes are organized into gene-dense and transcriptionally active compartment A and gene-sparse and transcriptionally inactive compartment B at the megabase scale \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Topologically associating domains (TADs) are formed at the sub-megabase scale, which functions as units for gene regulation \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Chromatin loops facilitate long-range interactions between enhancers and promoters for gene regulation within TADs \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The 3D organization of the genome is dynamically regulated in key biological processes such as stem cell differentiation \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, cell division \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and B-cell activation \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study comprehensively analyzes Hi-C, genomic resequencing, and RNA-seq across five multiple myeloma (MM) and control plasma cells. Consistent with previous study \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, we found that the 3D cancer genome is influenced by cancer-specific genome alterations and differential gene expression events. For the majority of these CNV related genes, a reduction in the number of detected interactions was observed. The level of interactions was found to be lower in multiple myeloma (MM) compared to the control groups. Many genes related to CNV were also found with loop difference, such as \u003cem\u003eGPR18\u003c/em\u003e and \u003cem\u003eGPR183\u003c/em\u003e. These two genes both showed CNV loss in MM patients. The \u003cem\u003eGPR18\u003c/em\u003e gene is related to the number of B cells and the expression of B cell-related genes \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, which may have clinical value for the prognosis of various cancers, including multiple myeloma. A recent study indicates that \u003cem\u003eGPR183\u003c/em\u003e is highly expressed in cell lines resistant to HHT (an anti-tumor drug), suggesting that it may be associated with drug resistance in tumor cells \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the compartment level, we found that more genes were activated, as there were 408 genes changed from compartment B state to A, and only 321 genes changed from compartment A to B. GO enrichment analysis (BP) of genes with A-to-B switching event showed that these inactive genes were involved in \u0026ldquo;homophilic cell adhesion via plasma membrane adhesion molecules \u0026rdquo; term, which indicated that there was a decrease function for \u0026ldquo;Cell-cell adhesion via plasma-membrane\u0026rdquo; term. The genes in this term include \u003cem\u003ePCDH9\u003c/em\u003e, \u003cem\u003eLRFN3\u003c/em\u003e, \u003cem\u003eIL1RAP\u003c/em\u003e, \u003cem\u003ePCDHA6\u003c/em\u003e, \u003cem\u003ePCDHA9\u003c/em\u003e, \u003cem\u003eCDHA8\u003c/em\u003e, \u003cem\u003ePCDHA7\u003c/em\u003e, \u003cem\u003ePCDHA5\u003c/em\u003e, \u003cem\u003ePCDHA4\u003c/em\u003e, \u003cem\u003ePCDHA2\u003c/em\u003e, \u003cem\u003ePCDHA1\u003c/em\u003e, \u003cem\u003ePCDHA13\u003c/em\u003e, \u003cem\u003ePCDHA11\u003c/em\u003e, \u003cem\u003ePCDHA10\u003c/em\u003e, \u003cem\u003ePCDHA12\u003c/em\u003e, and \u003cem\u003ePCDHA3.\u003c/em\u003e A previous study investigated the interaction between B9/BM1 cells and osteoclasts and showed the possibility of tumor metastasis in bone marrow \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. CML hematopoietic stem cells expressing \u003cem\u003eIL1RAP\u003c/em\u003e can be targeted by chimeric antigen receptor-engineered T cells \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, the inactivation of this gene might affect its function.\u003c/p\u003e \u003cp\u003eAt the TAD scale, consistent with the previous study \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, we also found that MM genomes contain more TADs (~\u0026thinsp;3627 in MM and 3197 in normal cells), and the average TAD size is smaller than in normal plasma cells (~\u0026thinsp;0.70 Mb in MM and 0.72 Mb in normal cells). It is recommended that heterogeneity of cancer cells may contribute to more diverse 3D genomes within a cell population, increasing the detected TAD numbers \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. These 43 genes were found in MM-specific TADs. The genes were mainly related to \u0026ldquo;Human immunodeficiency virus 1 infection\u0026rdquo; (including genes \u003cem\u003eTNFRSF1B\u003c/em\u003e, \u003cem\u003eFBXW11\u003c/em\u003e, \u003cem\u003eNFATC1\u003c/em\u003e and \u003cem\u003eAP1S3\u003c/em\u003e), \u0026ldquo;Wnt signaling pathway\u0026rdquo; (including genes \u003cem\u003eFBXW11\u003c/em\u003e, \u003cem\u003eNFATC1\u003c/em\u003e, \u003cem\u003eRSPO3\u003c/em\u003e, and \u003cem\u003eFZD9\u003c/em\u003e), and \u0026ldquo;Cytokine\u0026thinsp;\u0026minus;\u0026thinsp;cytokine receptor interaction\u0026rdquo; (including genes \u003cem\u003eTNFRSF1B\u003c/em\u003e, \u003cem\u003eTNFRSF8\u003c/em\u003e, \u003cem\u003eIL19\u003c/em\u003e, \u003cem\u003eIL20\u003c/em\u003e, and \u003cem\u003eIL24\u003c/em\u003e) KEGG pathways. Receptor activator of nuclear factor (NF)-κΒ ligand stimulation in multiple myeloma-derived osteoclasts induced elevated \u003cem\u003eNFATC1\u003c/em\u003e, and selenoprotein W into the nucleus as compared to that in the control cells \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Our results showed that genes in the cytokine-cytokine receptor interaction KEGG pathway, including \u003cem\u003eIL19\u003c/em\u003e, \u003cem\u003eIL20\u003c/em\u003e, and \u003cem\u003eIL24\u003c/em\u003e changed their 3D structures, which could regulate the immune system and have inflammation effects \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Interleukin-20 (IL-20) is a pro-inflammatory cytokine with diverse angiogenic properties, serum IL-20 concentrations were found to participate actively in the pathophysiology of MM progression \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The presence of IL24 might affect tumor growth using a mouse model, but not as much as the therapeutic effect of HSV-tk, injection of IL24 reduced the tumor size \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. At the loop level, these differentiated loops involved 113 genes, mainly in \u0026ldquo;AGE-RAGE signaling pathway in diabetic complications\u0026rdquo; and \u0026ldquo;Autophagy\u0026ndash;animal\u0026rdquo; KEGG pathways. The genes were mainly \u003cem\u003eMAPK10\u003c/em\u003e, \u003cem\u003eEGFLAM\u003c/em\u003e, \u003cem\u003eCOL4A5\u003c/em\u003e, \u003cem\u003eGAD1\u003c/em\u003e, \u003cem\u003eARSB\u003c/em\u003e, \u003cem\u003eMNX1\u003c/em\u003e, \u003cem\u003eGUCY2F\u003c/em\u003e, \u003cem\u003ePABPC3\u003c/em\u003e, \u003cem\u003eCIR1\u003c/em\u003e, \u003cem\u003eCPLX1\u003c/em\u003e, \u003cem\u003eWDR36\u003c/em\u003e, \u003cem\u003ePITX2, NKX3-1\u003c/em\u003e, \u003cem\u003eGATA4\u003c/em\u003e, and \u003cem\u003ePOM121L2\u003c/em\u003e. The identified chromatin interactions and differential gene expression patterns provide insights into the molecular mechanisms of MM, highlighting pathways such as immune response and cell adhesion that could be targeted therapeutically. These findings suggest that drugs interfering with these pathways or modulating the expression of key genes involved in MM pathogenesis might serve as potential therapeutic targets.\u003c/p\u003e \u003cp\u003eIn addition, by integrating 3D genome data, we found that genes such as \u003cem\u003eNSG2\u003c/em\u003e, \u003cem\u003eALDH1A2\u003c/em\u003e, and \u003cem\u003eCALCRL\u003c/em\u003e showed a higher expression in the MM patients. \u003cem\u003eNSG2\u003c/em\u003e is a member of the neuron-specific gene (NSG) family, which is specifically expressed in neurons [17] and localized to the plasma membrane, the trans-Golgi network, and multiple endolysosomal compartments \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The aldehyde dehydrogenase 1 (ALDH1) family contains major enzymes that produce retinoic acid by the oxidation of all-trans-retinal and 9-cis-retinal, which mainly participates in biological functions such as cell differentiation, apoptosis, cell cycle arrest, and eventually \u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. A previous study showed that \u003cem\u003eALDH1A2\u003c/em\u003e expression is regulated by the epigenetic regulation of DNMTs, and subsequently might act as a tumor suppressor in ovarian cancer \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. CALCRL is a G protein-coupled receptor that regulates the concentration of calcium ions in cells \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. It could inhibit cell proliferation and angiogenesis \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. CALCRL also contributes to the drug resistance in AML by controlling the ADM-CALCRL axis \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eARHGAP24\u003c/em\u003e showed a higher expression in the control. The Rho GTPase activating protein 24 (ARHGAP24) has been reported as a tumor suppressor in multiple cancers \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. These results indicated that the \u003cem\u003eNSG2\u003c/em\u003e, \u003cem\u003eALDH1A2\u003c/em\u003e, \u003cem\u003eCALCRL\u003c/em\u003e, and \u003cem\u003eARHGAP24\u003c/em\u003e genes might also have important potential functions during the multiple myeloma process. In addition, this study's findings may be limited by a relatively small sample size of multiple myeloma patients with the same symptom, age and sex, which could affect the generalizability of the results. Meanwhile, recent studies \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e have shown that single-cell RNA sequencing is a crucial technique for investigating cellular heterogeneity, highlighting the need for its incorporation in future research. Further studies should consider there are more than 2 MM patients and controls with the same sex, age and symptoms for analysis.\u003c/p\u003e \u003cp\u003eCombining 3D genome, genome, and transcriptome analyses, we reveal that during MM development, multiple levels of alterations, such as spatial genome reorganization, occur accompanied by gene expression. The 3D genomic architecture identified in this study provides a foundation for exploring the spatial organization of genes and their regulatory elements in MM, which could lead to the development of targeted therapies that disrupt specific chromatin interactions crucial for the disease's progression. For instance, the identification of key genes such as \u003cem\u003eNSG2\u003c/em\u003e, \u003cem\u003eALDH1A2\u003c/em\u003e, and \u003cem\u003eCALCRL\u003c/em\u003e, which show differential expression in MM patients, suggests potential avenues for therapeutic intervention, possibly through small molecule inhibitors or gene editing technologies. Furthermore, understanding the 3D genome's role in antigen presentation, as indicated by the involvement of MHC class II protein complex assembly, could pave the way for novel immunotherapies that enhance the immune system's ability to recognize and attack myeloma cells. However, since cancer types are diverse and alterations are heterogeneous, the phenomena observed in one may not hold in other cancer types. In the future, we need to investigate whether these observations are universal phenomena across cancer types.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePlasma Cell Identification\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, MACSprep Multiple Myeloma CD138 MicroBeads (Order no. 130-111-744) have been developed to positively select CD138\u0026thinsp;+\u0026thinsp;cells directly from whole blood. The operation method is modified according to the manufacturer's manual, and the details are described in the following process.\u003c/p\u003e \u003cp\u003eAdjust Cell Concentration: Ensure the specimen is qualified, with no hemolysis or coagulation; Use an automated hematology analyzer to measure the white blood cell count and calculate the suitable cell number: Suitable Cell Number\u0026thinsp;=\u0026thinsp;White Blood Cell Concentration (WBC) \u0026times; Specimen Volume; Calculate the volume of cells to be added per tube (\u0026micro;L)\u0026thinsp;=\u0026thinsp;1\u0026times;10\u003csup\u003e6\u003c/sup\u003e / Cell Concentration.\u003c/p\u003e \u003cp\u003eCell Surface Marker Staining: Label the tubes according to the experiment number and the antibody combination to be tested; Add the pre-prepared cocktail reagent to each tube for the chosen antibody combination; Mix the specimen well (at least 5 times by inversion), and add the calculated volume of cells to the bottom of the tube, mix well, and incubate in the dark for 15\u0026ndash;20 minutes; Add 500\u0026micro;l of lysing solution, place in the dark for 10 minutes until complete hemolysis occurs; Centrifuge at 2000 rpm for 3 minutes, discard the supernatant; Add 2mL of 1% newborn calf serum in PBS buffer to resuspend the cells, mix well, centrifuge at 2000 rpm for 3 minutes, and discard the supernatant; Add an appropriate amount (about 200\u0026ndash;600\u0026micro;L) of 1% paraformaldehyde fixative, resuspend the cells; Filter through a regular mesh with a pore size greater than 200 mesh, observe with the naked eye until no visible flocculent material or precipitate is present, and re-filter if necessary before preparing for machine detection.\u003c/p\u003e \u003cp\u003eIntracellular Staining of Samples: Add 0.5 mL of 1\u0026times; FACS permeabilizing solution, mix well, and incubate at room temperature in the dark for 5 minutes; Add 2 mL of PBS, mix well, centrifuge at 2000 rpm for 3 minutes, and discard the supernatant; Mix the cells, add the standard amount of fluorescent McAb against intracellular antigens, mix well, and incubate at room temperature in the dark for 30 minutes; Add 2mL of PBS, mix well, centrifuge at 2000 rpm for 3 minutes, and discard the supernatant; Add an appropriate amount (about 200\u0026ndash;600\u0026micro;L) of 1% paraformaldehyde fixative, mix well, and store in the dark at 2\u0026ndash;8\u0026deg;C, analyze within 24 hours; Filter through a regular mesh with a pore size greater than 200 mesh, observe with the naked eye until no visible flocculent material or precipitate is present, and re-filter if necessary before preparing for machine detection.\u003c/p\u003e \u003cp\u003ePlasma Cell Enrichment: CD138 Magnetic Bead Method: Use 1-2mL of Running buffer to moisten the purple blood filter plug; Take 2-4ml of blood into the filtration set and mark the blood level in a 15mL tube; After centrifugation at 1400r/min for 10 minutes, discard the supernatant and part of the settled red blood cells; Add Running buffer up to the mark, then add 100\u0026ndash;150\u0026micro;L of magnetic beads, mix slightly, and stand for 15 minutes; Use the magnetic bead column, first moisten with Running buffer, then filter the blood sample; Take Running buffer to the mark in a large tube, wash three times; Remove the magnetic bead column, add 5mL of Elution buffer, and quickly press down with a gas plug; Centrifuge at 1400r/min for 10 minutes and discard the supernatant.\u003c/p\u003e \u003cp\u003eFish Detection: the enriched plasma cells were exposed to 2\u0026times;SSC, 2\u0026times;SSC, 70% ethanol, 80% ethanol and 100% ethanol for 3min, and then dried. Adding Probes, Hybridizing: Denaturing at 88℃ for 2min, hybridizing at 45℃ for 2-16h; 2\u0026times;SSC at room temperature for 1min and in 0.3%NP-40/0.4\u0026times;SSC solution preheated at 68℃ for 2 min; Preheated distilled water at 37℃ for 1min and naturally dried in the dark; Add 10\u0026micro;L of hybrid blue staining solution to the target area of the slide, and cover the slide. Select the appropriate filter to observe under the fluorescence microscope.\u003c/p\u003e \u003cp\u003ePlasma Cell Freezing and Thawing, Freezing: Place the collection tube containing plasma cells into liquid nitrogen for rapid freezing and store it in a -80\u0026deg;C refrigerator for future use. Thawing: Place the plasma cell freezing tube in the \u0026minus;\u0026thinsp;80\u0026deg;C refrigerator on ice. Continuously shake in a 37\u0026deg;C constant-temperature water bath until the cells are thawed and ready for use.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHi-C library construction, quality control and sequencing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCell cross-linking: the sample was fixed with formaldehyde, and the intracellular protein was cross-linked with DNA to preserve the interaction and maintain the 3D structure of the cell. Endonuclease digestion: DNA was digested by restriction endonuclease to produce sticky ends on both sides of the cross-linking. The restriction enzyme used in this project is DpnII. Terminal repair: Biotin-labeled bases are introduced to facilitate the purification and capture of subsequent DNA using the mechanism of terminal repair. Cyclization: cyclization of the DNA repaired at the end, and cyclization between the DNA fragments containing interactions, to ensure that the location of the interaction DNA is determined in the process of subsequent sequencing and analysis. DNA purification and capture: the DNA was de-crosslinked, and the purified DNA was broken into 300bp-700bp fragments. The DNA fragments containing the interaction were captured by streptavidin magnetic beads and the library was constructed. After the construction of the library, the concentration of the library and the size of the inserted fragment (Insert Size) were detected by Qubit 2.0 and Agilent 2100, respectively, and the effective concentration of the library was quantified accurately by the Q-PCR method to ensure the quality of the library. After passing the library inspection, high-throughput sequencing was carried out on the Illumina platform, and the sequencing read length was PE150.\u003c/p\u003e \u003cp\u003eRaw data (raw reads) of fastq format were first processed through in-house Perl scripts. In this step, clean data were obtained by removing reads containing adapter, reads containing ploy-N, and low-quality reads from raw data. At the same time, Q20, Q30, and GC content of the clean data were calculated. All the downstream analyses were based on clean, high-quality data. Clean Reads used BWA (Burrows-Wheeler Aligner, v0.7.17) \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e to compare the two-terminal sequencing data with the reference genome sequences, respectively. The reads that can be compared are called Mapped Reads. Alignment efficiency refers to the percentage of Mapped Reads in Clean Reads. Using BWA \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e with the command \u0026ldquo;mem -t 10-k 32,\u0026rdquo; to align the two-terminal sequencing data with the assembled genome sequence to obtain the only Read. Then using HiC-Pro (An optimized and flexible pipeline for Hi-C data processing, v2.10.0) \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e to analyze the alignment results, identify the Valid Interaction Pairs and Invalid Interaction Pairs. We used HiC-Pro v2.10.0 \u003csup\u003e54\u003c/sup\u003e to obtain the corresponding standardized interaction matrix at various resolutions (10-, 20-, 100-kb, and 500-kb), and then calculate the Pearson correlation coefficient among the five samples.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eResolution analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSequencing depth of data determines the resolution of Hi-C data (the size of bin). Different resolutions should be used to study different sequencing depths of data. The resolution is calculated based on the method in reference \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. That is, the number of interactive reads between each bin is arranged from high to low according to the different sizes of the bin. When 80% of the bin is arranged, the bin still covers the size of the smallest bin with more than 1000 reads, satisfying this condition, that is, the resolution of the Hi-C library. The Hi-C data are standardized by using HiC-Pro v2.10.0 \u003csup\u003e54\u003c/sup\u003e software. Then the relationship between interaction frequency and genome linear distance is calculated by HiCdat \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The slope of the model is the corresponding IDEs (Interaction decay exponents). In the measured samples, it can be observed that the interactive signal decreases rapidly with the increase in distance. The mds algorithm of Pastis software is used to simulate the three-dimensional location of chromatin \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The software generates pdb (protein data bank) files based on Poisson distribution and uses pymol software to visualize the three-dimensional structure.\u003c/p\u003e \u003cp\u003eThe PCA (principal component analysis) was calculated by HiTC v1.24.0 software \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e with a bin size of 100 Kb. The PCA value was positive for the A compartment with high gene density and negative for the B compartment with low gene density. If there is a biological repetition for each sample, we merge the data from each library and identify A and B.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTAD and loop analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe identification of topology-related domains (TAD) was carried out according to bin\u0026thinsp;=\u0026thinsp;40 Kb using TadLib \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e software. At the same time, the software HOMER (Hypergeometric Optimization of Motif EnRichment, v5.1) \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e was used to calculate the ratio of IR (Inclusion Ratio), retain the TAD with IR\u0026thinsp;\u0026gt;\u0026thinsp;1, and filter out the TAD with a length of less than 5 bins to obtain the final TAD. The DI values of each sample were calculated. Finally, the DI delta value of each TAD is further calculated (that is, the average difference of DI of 4 bins upstream and downstream of the TAD boundary). Then, the limma package was used to calculate the difference between the control and MM samples. Only when the FDR value of difference significance was less than 0.01, and the DI delta score of the two groups of samples was not all greater than 200 could it be considered as a TAD boundary of difference.\u003c/p\u003e \u003cp\u003eLoop identification was using Juicer (v2.0) \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e for analysis with bin size\u0026thinsp;=\u0026thinsp;25 Kb, FDR\u0026thinsp;\u0026le;\u0026thinsp;0.01. To compare the loop structures between different samples, we first merge the loops from each sample \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, remove redundancy from the merged loops of all samples, and then calculate the original interaction values for each sample's non-redundant loops as input for edgeR (v3.8.6) \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Loops with an FDR value less than 0.01, a significant \u003cem\u003eP\u003c/em\u003e value less than 0.05, and an FC (Fold Change) greater than 1.5 are considered as differential loops.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene ontology analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe gene ontology (GO) enrichment analysis was carried out by using clusterProfiler R software package, and the hypergeometric test was used to find the GO items which were significantly enriched compared with the whole genome background. KEGG (Kyoto Encyclopedia of Genes and Genomes) \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e pathway enrichment analysis was also conducted. We used KOBAS (KEGG Orthology Based Annotation System, v3.0) \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e software to test the statistical enrichment of differential expression genes in KEGG pathways. We used clusterProfiler R packages to find KEGG pathways that are significantly enriched compared to the entire genome background.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWhole-genome sequencing experiments and analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe whole genome DNA of the five MM patients was extracted and sequenced at an average 37.5\u0026times; depth through XTen (Illumina). The sequenced reads were mapped to the human reference genome (hg19) by the bwa-mem software \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Only uniquely mapped reads were used for downstream analysis. The Picard software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://broadinstitute.github.io/picard\u003c/span\u003e\u003cspan address=\"http://broadinstitute.github.io/picard\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to remove PCR duplicates. The total mapping rate is above 90% for each sample. The detection of SNP (Single Nucleotide Polymorphism) and small INDEL (small Insertion and Deletion) is mainly implemented by GATK software (The Genome Analysis Toolkit, v4.0) \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. We next used Manta (v1.6.0) \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e to identify genomic structural variation (SV).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRNA-seq experiments and analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe use the TRIzol Reagent (Life Technologies, California, USA) instruction manual to extract total RNA from cells. Use the NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE) to measure the concentration and purity of RNA. Evaluate the integrity of the RNA using the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA) with the RNA Nano 6000 Kit. The RIN (RNA integrity number) values ranged from 8.0 to 9.4. Indicating that all sample\u0026rsquo;s RNA were qualified, the quality meets the requirements of database construction, the total amount meets two or more conventional quantitative databases. Magnetic beads coated with oligo(dT) are used to selectively capture mRNA from total RNA. First-strand cDNA synthesis is then performed, followed by the synthesis of the second strand of cDNA. The overhangs at the ends of the cDNA are repaired to create blunt ends through the activity of exonucleases and polymerases. After the 3' ends of the DNA fragments are adenylated, NEBNext adapters with a hairpin loop structure are ligated to them. The library fragments are then purified using AMPure XP magnetic beads from Beckman Coulter in Beverly, USA. Subsequently, 3\u0026micro;l of USER Enzyme from NEB (USA) is added, and the mixture is incubated at 37\u0026deg;C for 15 minutes. Before proceeding to PCR, the reaction is heated at 95\u0026deg;C for 5 minutes to denature any remaining secondary structures. The PCR is then carried out using a high-fidelity DNA polymerase, along with universal PCR primers and index (X) primers. Finally, the PCR products are purified once more using AMPure XP magnetic beads, and the quality of the library is assessed using an Agilent Bioanalyzer 2100 system. The sequenced reads were mapped to the human reference genome (hg19) by TopHat (v2.1.1) \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and gene expressions were quantified by Cufflinks (v2.2.1) \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. We used the RStudio software for the downstream statistical analyses. PUREE \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e was used to test the tumor purity of these MM samples.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn summary, we have investigated the 3D genome of MM and analyzed the differences at compartment, TADs, loops, genomes, and gene expression levels to identify MM-related pathways and key genes. These findings extend our understanding of MM, which may implicate in clinical treatment and drug development for MM.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics statement\u003c/strong\u003e \u003cp\u003eAll methods carried out in accordance with the relevant guidelines and regulations. This study has been approved by the Ethics Committee of Chengdu First People's Hospital, ethics number ZXKY No.002, in 2020. All patients and blood donors in this study have signed informed consent forms by themselves or their guardian(s).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by The Science and Technology Department of Sichuan Province (No.2020YJ0438).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConflict of interest statement\u003c/b\u003e: The authors declare no conflicts of interest regarding this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, K.Z.,Y.L., and X.F.; writing\u0026mdash;original draft preparation, K.Z., M.C.(Mengsi Chen) ,M.C.(Ming Chen) ,Y.W. and H.L.; writing\u0026mdash;review and editing, Y.L., X.G., L.L.,L.T.,X.L., D.H., and X.F.; Supervision, X.L., D.H., and X.F.; funding acquisition, K.Z.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availability: The accession number for the WGS, RNA-seq, and HiC data sets generated in the study is HRA007587via GSA (https://ngdc.cncb.ac.cn/gsa/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJune, C. 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It is an incurable disease, accounting for about 10% of blood malignancies and the second most common hematologic malignancy. Therefore, in-depth research into the molecular mechanisms and therapeutic targets of the disease is crucial. For the first time, we performed high-throughput chromosome conformation capture (Hi\u0026ndash;C) analysis of plasma cells in five multiple myeloma patients, and integrated it with genome resequencing and transcriptomic associated with genomic variation and gene expression. As a result, 19 specific TAD (Topologically Associating Domain) boundaries in MM samples related to the immune response and Wnt signaling pathways were identified. Additionally, Loop structures were also analyzed, revealing that promoter-enhancer-associated loops were the most prevalent. Genomic characteristics of MM patients were explored, identifying SNPs, InDels, and CNVs, with variations in the CDS region potentially affecting gene function. Transcriptome analysis showed differentially expressed genes in MM patients, mainly involved in p53 signaling and cell adhesion. Multi-omics analysis identified overlapping genes related to MM, including those involved in MHC class II protein complex assembly and antigen presentation. The study provides insights into the complex genomic and transcriptomic changes in MM plasma cells, potentially aiding in identifying therapeutic targets.\u003c/p\u003e","manuscriptTitle":"The 3D genome of plasma cells in multiple myeloma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 05:34:34","doi":"10.21203/rs.3.rs-5663072/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T12:04:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-19T05:16:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222805264620200406055872226276632527389","date":"2025-04-08T07:27:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-08T07:23:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-07T06:11:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-30T15:30:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cc8a46a-5032-4f23-aee6-44bb99f1159e","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46854659,"name":"Biological sciences/Cancer"},{"id":46854660,"name":"Biological sciences/Molecular biology"},{"id":46854661,"name":"Health sciences/Medical research"},{"id":46854662,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2025-06-09T16:08:22+00:00","versionOfRecord":{"articleIdentity":"rs-5663072","link":"https://doi.org/10.1038/s41598-025-03132-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-06-02 15:57:07","publishedOnDateReadable":"June 2nd, 2025"},"versionCreatedAt":"2025-04-10 05:34:34","video":"","vorDoi":"10.1038/s41598-025-03132-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-03132-2","workflowStages":[]},"version":"v1","identity":"rs-5663072","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5663072","identity":"rs-5663072","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}