Genomic Regions Research Articles

High-throughput root phenotyping and association analysis identified potential genomic regions for phosphorus use efficiency in wheat (Triticum aestivum L.)

High-throughput root phenotyping and association analysis identified potential genomic regions for phosphorus use efficiency in wheat (Triticum aestivum L.)

Cadmium (Cd) Tolerance and Phytoremediation Potential in Fiber Crops: Research Updates and Future Breeding Efforts

Heavy metal pollution is one of the most devastating abiotic factors, significantly damaging crops and human health. One of the serious problems it causes is a rise in cadmium (Cd) toxicity. Cd is a highly toxic metal with a negative biological role, and it enters plants via the soil–plant system. Cd stress induces a series of disorders in plants’ morphological, physiological, and biochemical processes and initiates the inhibition of seed germination, ultimately resulting in reduced growth. Fiber crops such as kenaf, jute, hemp, cotton, and flax have high industrial importance and often face the issue of Cd toxicity. Various techniques have been introduced to counter the rising threats of Cd toxicity, including reducing Cd content in the soil, mitigating the effects of Cd stress, and genetic improvements in plant tolerance against this stress. For decades, plant breeders have been trying to develop Cd-tolerant fiber crops through the identification and transformation of novel genes. Still, the complex mechanism of Cd tolerance has hindered the progress of genetic breeding. These crops are ideal candidates for the phytoremediation of heavy metals in contaminated soils. Hence, increased Cd uptake, accumulation, and translocation in below-ground parts (roots) and above-ground parts (shoots, leaves, and stems) can help clean agricultural lands for safe use for food crops. Earlier studies indicated that reducing Cd uptake, detoxification, reducing the effects of Cd stress, and developing plant tolerance to these stresses through the identification of novel genes are fruitful approaches. This review aims to highlight the role of some conventional and molecular techniques in reducing the threats of Cd stress in some key fiber crops. Molecular techniques mainly involve QTL mapping and GWAS. However, more focus has been given to the use of transcriptome and TFs analysis to explore the potential genomic regions involved in Cd tolerance in these crops. This review will serve as a source of valuable genetic information on key fiber crops, allowing for further in-depth analyses of Cd tolerance to identify the critical genes for molecular breeding, like genetic engineering and CRISPR/Cas9.

Empirical versus estimated accuracy of imputation: optimising filtering thresholds for sequence imputation.

Genotype imputation is a cost-effective method for obtaining sequence genotypes for downstream analyses such as genome-wide association studies (GWAS). However, low imputation accuracy can increase the risk of false positives, so it is important to pre-filter data or at least assess the potential limitations due to imputation accuracy. In this study, we benchmarked three different imputation programs (Beagle 5.2, Minimac4 and IMPUTE5) and compared the empirical accuracy of imputation with the software estimated accuracy of imputation (Rsqsoft). We also tested the accuracy of imputation in cattle for autosomal and X chromosomes, SNP and INDEL, when imputing from either low-density or high-density genotypes. The accuracy of imputing sequence variants from real high-density genotypes was higher than from low-density genotypes. In our software benchmark, all programs performed well with only minor differences in accuracy. While there was a close relationship between empirical imputation accuracy and the imputation Rsqsoft, this differed considerably for Minimac4 compared to Beagle 5.2 and IMPUTE5. We found that the Rsqsoft threshold for removing poorly imputed variants must be customised according to the software and this should be accounted for when merging data from multiple studies, such as in meta-GWAS studies. We also found that imposing an Rsqsoft filter has a positive impact on genomic regions with poor imputation accuracy due to large segmental duplications that are susceptible to error-prone alignment. Overall, our results showed that on average the imputation accuracy for INDEL was approximately 6% lower than SNP for all software programs. Importantly, the imputation accuracy for the non-PAR (non-Pseudo-Autosomal Region) of the X chromosome was comparable to autosomal imputation accuracy, while for the PAR it was substantially lower, particularly when starting from low-density genotypes. This study provides an empirically derived approach to apply customised software-specific Rsqsoft thresholds for downstream analyses of imputed variants, such as needed for a meta-GWAS. The very poor empirical imputation accuracy for variants on the PAR when starting from low density genotypes demonstrates that this region should be imputed starting from a higher density of real genotypes.

Identification and validation of two quantitative trait loci showing pleiotropic effect on multiple grain-related traits in bread wheat (Triticum aestivum L.).

QKl/Tgw/Gns.yaas-2D associates with KL, TGW, and GNS, and QKl/Tgw.yaas-5A associates with KL and TGW. Significantly pleiotropic and additive effects of these two QTL were validated. The YM5 allele both at QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A was proved to be the best allelic combination for improving yield potential. Kernel length (KL), kernel width (KW), thousand grain weight (TGW), and grain number per spike (GNS) play important roles in the yield improvement of wheat. In this study, one recombinant inbred line (RIL) derived from a cross between Yangmai 5 (YM5) and Yanzhan 1 (YZ1) was used to identify quantitative trait loci (QTL) associated with KL, KW, TGW, and GNS across three years. Two pleiotropic QTL namely QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A were located in two genomic regions on chromosomes 2D and 5A, respectively. Breeder-friendly Kompetitive Allele-Specific PCR (KASP) markers for QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A were developed and validated in a set of 246 wheat cultivars/lines. Analysis of allelic combinations indicated that the YM5 allele both at QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A is probably the best one to promote TGW, GNS, and grain weight per spike. Based on the analysis of gene annotation, sequence variations, expression patterns, and GO enrichment, twenty-five and twenty-four candidate genes of QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A, respectively, were identified. These results provide the basis of fine-mapping the target QTL and marker-assisted selection in wheat yield-breeding programs.

Chromosomal rearrangements associated with SMC5/6 deficiency in DNA replication.

Completion of DNA replication before chromosome segregation is essential for the stable maintenance of the genome. Under replication stress, DNA synthesis may persist beyond S phase, especially in genomic regions that are difficult to proceed with the replication processes. Incomplete replication in mitosis emerges as non-disjoined segment in mitotic chromosomes leading to anaphase bridges. The resulting chromosome rearrangements are not well characterized, however. Here, we report that incomplete replication due to SMC5/6 deficiency impairs sister chromatid disjunction at difficult-to-replicate regions, including common fragile sites. These non-disjoined regions manifest as cytologically defined symmetric gaps, causing anaphase bridges. These bridges break at the gaps, leading to telomere loss, micronucleation, and fragmentation. Subsequently, fusions between telomere-deficient chromosomes generate complex chromosomal rearrangements, including dicentric chromosomes, suggesting the occurrence of breakage-fusion-bridge cycle. Additionally, chromosomes in micronuclei were pulverized, indicative of chromothripsis. Our findings suggest that incomplete replication facilitates complex chromosomal rearrangements, which may contribute to genomic instability in human cancers.

A conserved sequence that sparked the field of evo-devo.

The discovery that homeotic genes in Drosophila are conserved and utilized for embryonic development throughout the animal kingdom, including humans, revolutionized the fields of developmental biology and evolutionary developmental biology (evo-devo). In a pair of back-to-back papers published in Cell in 1984, researchers at the Biozentrum in Basel, Switzerland, showed that the homeobox - previously identified as a sequence shared by homeotic genes in Drosophila - was also present in the genome of diverse animals. The first paper (McGinnis et al., 1984a) showed that genomes of both invertebrates and vertebrates contain sequences that cross-hybridized with Drosophila homeobox probes. The second paper (Carrasco et al., 1984) identified a cross-hybridizing sequence in the model system Xenopus laevis. They then isolated the first vertebrate homeobox-containing gene by cloning and sequencing of the corresponding genomic region. Finally, they showed that this gene (AC1, later renamed HoxC6) was expressed during embryonic development, the first evidence that developmentally expressed Drosophila genes could be used to isolate regulators of vertebrate embryonic development. These findings led to a flurry of activity in the evo-devo field, initially focused on isolating Hox genes across diverse species, and then expanding to isolation of other gene families based on Drosophila orthologs, an approach that continues today. This led to the notion of a conserved genetic toolkit for embryonic development, currently accepted, but unexpected at the time of its discovery. We attempt to provide some context for the sea-change in thinking that these discoveries brought about by referring to Jean Piaget's theories about the sequential acquisition of scientific knowledge.

Unveiling bat-borne viruses: a comprehensive classification and analysis of virome evolution.

Bats (Order Chiroptera) are an important reservoir of emerging zoonotic microbes, including viruses of public health concern such as henipaviruses, lyssaviruses, and SARS-related coronaviruses. Despite the continued discovery of new viruses in bat populations, a significant proportion of these viral agents remain uncharacterized, highlighting the imperative for additional research aimed at elucidating their evolutionary relationship and taxonomic classification. In order to delve deeper into the viral reservoir hosted by bats, the present study employed Next Generation Sequencing (NGS) technology to analyze 13,105 swab samples obtained from various locations in China. Analysis of 378 sample pools revealed the presence of 846 vertebrate-associated viruses. Subsequent thorough examination, adhering to the International Committee on Taxonomy of Viruses (ICTV) criteria for virus classification, identified a total of 120 putative viral species with the potential to emerge as novel viruses, comprising a total of 294 viral strains. Phylogenetic analysis of conserved genomic regions indicated the novel virus exhibited a diverse array of viral lineages and branches, some of which displayed close genetic relationships to known human and livestock pathogens, such as poxviruses and pestiviruses. This study investigates the breadth of DNA and RNA viruses harbored by bats, delineating several novel evolutionary lineages and offering significant contributions to virus taxonomy. Furthermore, the identification of hitherto unknown viruses with relevance to human and livestock health underscores the importance of this study in encouraging infectious disease monitoring and management efforts in both public health and veterinary contexts. Video Abstract.

Detection of Brain-Derived Cell-Free DNA in Plasma

Background: Neuronal loss is a major pathological feature of neurodegenerative diseases. The analysis of plasma cell-free DNA (cfDNA) is an emerging approach to track cell death events in a minimally invasive way and from inaccessible areas of the body, such as the brain. Previous studies showed that DNA methylation (DNAm) profiles can be used to map the tissue of origin of cfDNA and to identify molecules released from the brain upon cell death. The aim of the present study is to contribute to this research field, presenting the development and validation of an assay for the detection of brain-derived cfDNA (bcfDNA). Methods: To identify CpG sites with brain-specific DNAm, we compared brain and non-brain tissues for their chromatin state profiles and genome-wide DNAm data, available in public datasets. The selected target genomic regions were experimentally validated by bisulfite sequencing on DNA extracted from 44 different autoptic tissues, including multiple brain regions. Sequencing data were analysed to identify brain-specific epihaplotypes. The developed assay was tested in plasma cfDNA from patients with immune effector cell-associated neurotoxicity syndrome (ICANS) following chimeric antigen receptor T (CAR-T) therapy. Results: We validated five genomic regions with brain-specific DNAm (four hypomethylated and one hypermethylated in the brain). DNAm analysis of the selected genomic regions in plasma samples from CAR-T patients revealed higher levels of bcfDNA in participants with ongoing neurotoxicity syndrome. Conclusions: We developed an assay for the analysis of bcfDNA in plasma. The assay is a promising tool for the early detection of neuronal loss in neurodegenerative diseases.

Inter-chromosomal contacts demarcate genome topology along a spatial gradient.

Non-homologous chromosomal contacts (NHCCs) between different chromosomes participate considerably in gene and genome regulation. Due to analytical challenges, NHCCs are currently considered as singular, stochastic events, and their extent and fundamental principles across cell types remain controversial. We develop a supervised and unsupervised learning algorithm, termed Signature, to call NHCCs in Hi-C datasets to advance our understanding of genome topology. Signature reveals 40,282 NHCCs and their properties across 62 Hi-C datasets of 53 diploid human cell types. Genomic regions of NHCCs are gene-dense, highly expressed, and harbor genes for cell-specific and sex-specific functions. Extensive inter-telomeric and inter-centromeric clustering occurs across cell types [Rabl's configuration] and 61 NHCCs are consistently found at the nuclear speckles. These constitutive 'anchor loci' facilitate an axis of genome activity whilst cell-type-specific NHCCs act in discrete hubs. Our results suggest that non-random chromosome positioning is supported by constitutive NHCCs that shape genome topology along an off-centered spatial gradient of genome activity.

Drought-tolerant wheat for enhancing global food security.

Wheat is among the most produced grain crops of the world and alone provides a fifth of the world's calories and protein. Wheat has played a key role in food security since the crop served as a Neolithic founder crop for the establishment of world agriculture. Projections showing a decline in global wheat yields in changing climates imply that food security targets could be jeopardized. Increased frequency and intensity of drought occurrence is evident in major wheat-producing regions worldwide, and notably, the wheat-producing area under drought is projected to swell globally by 60% by the end of the 21st century. Wheat yields are significantly reduced due to changes in plant morphological, physiological, biochemical, and molecular activities in response to drought stress. Advances in wheat genetics, multi-omics technologies and plant phenotyping have enhanced the understanding of crop responses to drought conditions. Research has elucidated key genomic regions, candidate genes, signalling molecules and associated networks that orchestrate tolerance mechanisms under drought stress. Robust and low-cost selection tools are now available in wheat for screening genetic variations for drought tolerance traits. New breeding techniques and selection tools open a unique opportunity to tailor future wheat crop with optimal trait combinations that help withstand extreme drought. Adoption of the new wheat varieties will increase crop diversity in rain-fed agriculture and ensure sustainable improvements in crop yields to safeguard the world's food security in drier environments.

Abstract A024: Evaluating the effect of plasma-related preanalytical variables on the robustness of the liquid biopsy early CRC detection Signal-C assay

Abstract Liquid biopsies and cell-free DNA (cfDNA) analysis have emerged as novel non-invasive diagnostic tools in oncology. Analysis of cfDNA methylation status has been proposed as promising approach for several diagnostic purposes, and robust methylation-detecting technologies are of primary importance for the clinical implementation of novel diagnostic assays. Signal-C is an accurate NGS-based methyl-sensitive assay detecting colorectal cancer (CRC) currently under clinical validation. In this study we assessed the influence of different preanalytical variables influencing plasma stability, as the storage time of plasma samples and the tube type used for blood collection, on Signal-C assay results to determine its robustness. 958 samples with 3ml to 4ml plasma were used for cfDNA extraction and run through Signal-C assay following UDX protocol. In brief, 8 to 20ng cfDNA were used as an input for methylation- specific library preparation, which was run according to manufacturer’s instructions. Hybrid- capture was performed to enrich libraries for genomic regions included in Signal-C assay and sequenced on Illumina Novaseq machines. Relevant sequencing QC metrics, such as depth filtered, read yield from raw to filtered and PCR duplicate proportion were calculated, together with cancer predictions according to Signal-C test. Pearson coefficient and Pearson's Chi- squared test with Yates' continuity correction have been used to assess correlations, and Wilcoxon test has been used to measure the significance of the differences between quality metrics with respect to the tube type. We have analyzed the influence of different plasma-related preanalytical variables on sequencing metrics and cancer predictions of Signal-C assay. Plasma age, calculated as the time elapsed between blood collection and cfDNA quantification, shows a small negative correlation with YIELD_RAW_FILTERED (R = - 0.08, p=0.013) and a small positive correlation with PCR_DUP_PROP (R = 0.098, p=0.0024), while it doesn't influence DEPTH_FILTERED values (p>0.05). Use of different blood collection tubes (EDTA or Streck tubes) influenced in a small but significative manner the YIELD_RAW_FILTERED and the PCR_DUP_PROP (p<0.001), while it doesn’t affect the DEPTH_FILTERED values (p>0.05). Despite the sequencing QC metrics being subject to small but statistically significant differences, the overall cancer classification performance of Signal-C assay was not affected by the changes in plasma age (Pearson's product-moment correlation, sensitivity p=0.87, specificity p=0.091) nor in tube type used (Pearson's Chi-squared test with Yates' continuity correction, p=0.219). Here we evaluated the effect of plasma’s storage time and the type of blood collection tube on Signal-C assay performance. While signal processing features show small differences between the tested variables, this does not affect overall clinical performance of the test, supporting its robustness. Additional studies to analyze the effect of other preanalytical variables on Signal-C performance are underway to establish its robustness. Citation Format: Fernando Trincado Alonso, Pol Canal Noguer, Kristi Kruusmaa, Arianna Bertossi. Evaluating the effect of plasma-related preanalytical variables on the robustness of the liquid biopsy early CRC detection Signal-C assay [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A024.

Abstract A014: A methylation state specific targeted background depletion technique for enrichment of ctDNA fraction

Abstract Liquid biopsy continues to be a focus of the cancer diagnostics industry, primarily due to the non-invasive nature of sample collection, however the low levels of circulating tumor DNA (ctDNA) remain a challenge. Methylation has emerged as a promising biomarker for detecting ctDNA, yet detecting the cancerous signal in an excess of non-cancerous, unmethylated cfDNA in the background has been proven difficult. Harbinger Health has developed a novel method that depletes unmethylated background at the CpG-level from specific genomic regions of interest, resulting in an enrichment of the ctDNA fraction. We developed a background cfDNA depletion method that utilizes the endonuclease activity and the specificity of CRISPR/Cas12a to remove unmethylated DNA fragments at ctDNA methylation biomarker sites. First, we identified cancer-informative regions that showed consistent contiguous elevated methylation states in cancer compared to normal samples. We then designed guide RNAs that specifically bind and cut unmethylated CpG sites within the target regions. Cas depletion of bisulfite-converted DNA enriches cancer informative signal for bioanalysis. To test validity of our Cas complex designs, we created model samples that mimic fully unmethylated and fully methylated species. We spiked these cfDNA models into a background of genomic DNA from 5% to 0.01%. In triplicate, we examined cutting efficiency, multiplexing and specificity of Cas to unmethylated species of model DNA. Results were assessed using qPCR by comparing differences in Ct with and without Cas treatment. Additionally, we applied this depletion step during our library preparation process to show utility in methylation analysis of low tumor content liquid biopsy samples. Using both the model sample dilution series and bisulfite converted cfDNA samples, which represent a range of cancerous and background methylation signal, we deplete unmethylated targets using Cas prior to indexing PCR. This rendered unmethylated targets non-amplifiable, removing them from analysis. Results were assessed using both qPCR and sequencing data, such as methylated read counts over the target region. We demonstrated our analytical approach efficiently depletes non-cancer cfDNA by on average 9-fold, which increases the fraction of ctDNA signal and can be multiplexed. This background signal depletion driven by CRISPR is sensitive and specific, maintaining methylated DNA signal detection down to 0.01% at inputs of 10ng DNA. Our Cas-mediated depletion of background signal method has proven useful for enriching rare methylated fragments over background by improving the signal to noise ratio. This technique is novel by harnessing the specificity of CRISPR to target DNA in both a sequence and methylation state specific manner. The depletion of precise methylation states at the CpG-level make it suitable for use in multiple bioassays. Citation Format: Caitlin Gilley, Miguel Williams, Emily Neaga, Sarah Falotico, Kade Pettie, Anthony Shuber. A methylation state specific targeted background depletion technique for enrichment of ctDNA fraction [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A014.

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Genome-Wide Scan for Selective Sweeps Reveals Novel Loci Associated with Prolificacy in Iranian Sheep Breeds in Comparison with Highly Prolific Exotic Breed

Domestication and selection significantly changed phenotypic traits in modern domestic animals. To identify the genomic regions associated with prolificacy in this study, 837 ewes from three Iranian indigenous sheep breeds, consisting of Baluchi, Lori-Bakhtiari, and Zandi uniparous breeds, and one Greek highly prolific dairy sheep, namely Chios, were genotyped using OvineSNP50K arrays. Statistical tests were then performed using different and complementary methods based on either site frequency (FST) and haplotype (hapFLK) between populations, followed by a pathway analysis of the genes contained in the selected regions. The results revealed that for the top 0.01 percentile of the obtained FST values, 16 genomic regions on chromosomes 2, 3, 4, 7, 8, 9, 13, 14, 16, 18, 19, and 20, and for hapFLK values, 3 regions located on chromosomes 3, 7, and 13, were under selection. A bioinformatic analysis of these genomic regions showed that these loci overlapped with potential candidate genes associated with prolificacy in sheep including GNAQ, COL5A2, COL3A1, HECW1, FBN1, COMMD3, RYR1, CCL28, SERPINA14, and HSPA2. These regions also overlapped with some quantitative trait loci (QTLs) linked to prolificacy traits, milk yield, and body weight. These findings suggest that future research could further link these genomic regions to prolificacy traits in sheep.

Comparative genomics provides insights into adaptive evolution and demographics of bats.

Bats possess a range of distinctive characteristics, including flight, echolocation, impressive longevity, and the ability to harbor various zoonotic pathogens. Additionally, they account for the second-highest species diversity among mammalian orders, yet their phylogenetic relationships and demographic history remain underexplored. Here, we generated de novo assembled genomes for 17 bat species and two of their mammalian relatives (the Amur hedgehog and Chinese mole shrew), with 12 genomes reaching chromosome-level assembly. Comparative genomics and ChIP-seq assays identified newly gained genomic regions in bats potentially linked to the regulation of gene activity and expression. Notably, some antiviral infection related gene under positive selection exhibited the activity of suppressing cancer, evidencing the linkage between virus tolerance and cancer resistance in bats. By integrating published bat genome assemblies, phylogenetic reconstruction established the proximity of noctilionoid bats to vesper bats. Interestingly, we found two distinct patterns of ancient population dynamics in bats and population changes since the last-glacial maximum do not reflect species phylogenetic relationships. These findings enriched our understanding of adaptive mechanisms and demographic history of bats.

Integrated eQTL mapping approach reveals genomic regions regulating candidate genes of the E8-r3 locus in soybean

Deciphering the gene regulatory networks of critical quantitative trait loci associated with early maturity provides information for breeders to unlock soybean’s (Glycine max (L.) Merr.) northern potential and expand its cultivation range. The E8-r3 locus is a genomic region regulating the number of days to maturity under constant short-day photoperiodic conditions in two early-maturing soybean populations (QS15524F2:F3 and QS15544RIL) belonging to maturity groups MG00 and MG000. In this study, we developed a combinatorial expression quantitative trait loci mapping approach using three algorithms (ICIM, IM, and GCIM) to identify the regions that regulate three candidate genes of the E8-r3 locus (Glyma.04G167900/GmLHCA4a, Glyma.04G166300/GmPRR1a, and Glyma.04G159300/GmMDE04). Using this approach, a total of 2,218 trans (2,061 genes)/7 cis (7 genes) and 4,073 trans (2,842 genes)/3,083 cis (2,418 genes) interactions were mapped in the QS15524F2:F3 and QS15544RIL populations, respectively. From these interactions, we successfully identified two hotspots (F2_GM15:49,385,092-49,442,237 and F2_GM18:1,434,182-1,935,386) and three minor regions (RIL_GM04:17,227,512-20,251,662, RIL_GM04:31,408,946-31,525,671 and RIL_GM13:37,289,785-38,620,690) regulating the candidate genes of E8-r3 and several of their homologs. Based on co-expression network and single nucleotide variant analyses, we identified ALTERED PHLOEM DEVELOPMENT (Glyma.15G263700) and DOMAIN-CONTAINING PROTEIN 21 (Glyma.18G025600) as the best candidates for the F2_GM15:49,385,092-49,442,237 and F2_GM18:1,434,182-1,935,386 hotspots. These findings demonstrate that a few key regions are involved in the regulation of the E8-r3 candidates GmLHCA4a, GmPRR1a, and GmMDE04.

Convergent degenerated regulatory elements associated with limb loss in limbless amphibians and reptiles.

Limbs are a defining characteristic of tetrapods, yet numerous taxa, primarily among amphibians and reptiles, have independently lost limbs as an adaptation to new ecological niches. To elucidate the genetic factors contributing to this convergent limb loss, we present a 12 Gb chromosome-level assembly of the Banna caecilian (Ichthyophis bannanicus), a limbless amphibian. Our comparative analysis, which includes the reconstruction of amphibian karyotype evolution, reveals constrained gene length evolution in a subset of developmental genes across three large genomes. Investigation of limb development genes uncovered the loss of Grem1 in caecilians and Tulp3 in snakes. Interestingly, caecilians and snakes share a significantly larger number of convergent degenerated conserved non-coding elements (dCNEs) than limbless lizards, which have a shorter evolutionary history of limb loss. These convergent dCNEs overlap significantly with active genomic regions during mouse limb development and are conserved in limbed species, suggesting their essential role in limb patterning in the tetrapod common ancestor. While most convergent dCNEs emerged in the jawed vertebrate ancestor, coinciding with the origin of paired appendage, more recent dCNEs also contribute to limb development, as demonstrated through functional experiments. Our study provides novel insights into the regulatory elements associated with limb development and loss, offering an evolutionary perspective on the genetic basis of morphological specialization.

BIOM-67. DIFFERENTIAL DNA METHYLATION PATTERNS IN THE CSF OF PATIENTS WITH DIFFUSE GLIOMAS

Abstract Studies have shown that different central nervous system (CNS) tumor types exhibit unique DNA methylation profiles. DNA methylation-based machine learning classification of CNS tumor tissue has been demonstrated to be highly-accurate in differentiating between tumor types, including between diffuse glioma subtypes. While similar analyses have been performed using cell-free DNA (cfDNA) from blood, few studies have looked at the potential of DNA methylation as a biomarker for diffuse gliomas using CSF-derived cfDNA. In this study, we analyzed CSF cfDNA methylation patterns in diffuse glioma patients. DNA methylation profiling of CSF of patients with glioblastoma, IDH-wildtype (GBM) (n=37), IDH-mutant diffuse gliomas (n=12), and non-neoplastic conditions (n=5) was performed using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). Methylation quantification was estimated for each genomic region. Significance testing was performed between diagnostic groups to discover differentially-methylated regions (DMRs). The 300 most differentially-methylated regions (DMRs) for each comparison (IDH-wildtype vs. IDH-mutant, IDH-mutant vs. control, IDH-wildtype vs. control), for a total of 900 DMRs, were utilized as features in dimensionality reduction analysis and machine learning. Out of ~1×107 defined genomic regions, there were ~7×105 DMRs between GBM and control samples and ~1.5×106 DMRs between GBM and IDH-mutant samples, accounting for ~7% and ~15% of the genome, respectively. Uniform manifold approximation and projection (UMAP) and hierarchical clustering analysis revealed that samples were mostly clustered by diagnosis. Random forest analysis of the 900 DMRs with an 80/20 train/test split resulted in an overall accuracy of 0.815. In conclusion, CSF cfDNA methylation profiles of diffuse glioma patients exhibit differential methylation patterns between IDH-mutant and IDH-wildtype tumors, as well as between tumor and control, and could be informative biomarkers for the diagnosis of patients with diffuse gliomas.

The chloroplast genome sequence and phylogenetic analysis of Rubia alata Wall and Rubia ovatifolia Z. Ying Zhang. (Rubiaceae).

Rubia alata Wall (R. alata) and Rubia ovatifolia Z. Ying Zhang (R. ovatifolia) are unique medicinal plants native to China. Sequencing their chloroplast genomes is important for understanding species differentiation and establishing phylogenetic relationships. The chloroplast genomes of R. alata and R. ovatifolia were sequenced using the Illumina HiSeq platform. The chloroplast genome of R. alata is 154,973 base pairs (bp) in length, containing a large single-copy region (LSC) of 84,801bp, a small single-copy region (SSC) of 17,138bp, and a pair of inverted repeats (IRs) of the same length. The length of the chloroplast genome, LSC, SSC, and IR regions of R. ovatifolia is 26,517bp, 84,716bp, 17,116bp and 26,517bp, respectively. Codon usag e analysis revealed that R. alata had the highest frequency of Aspartic acid (Asp) (1650 occurrences) in protein-coding sequences (CDS), while R. ovatifolia showed the highest frequency of Tyrosine (Try) (1479 occurrences). Comparative analysis of chloroplast genomes across seven species from the genus Rubia identified the most divergent coding regions, including rps16, psbI-trns-CGA, and petN, while plastid rRNAs were the most conserved. Phylogenetic analysis showed R. alata clustering with R. cordifolia (66.3% support), and R. ovatifolia clustering with Rubia podantha (100% support). These findings enhance our understanding of the chloroplast genome structure in Rubia species and provide molecular information for the future development and utilization of R. alata and R. ovatifolia resources.

Personalized MRD Assessment in Peri-surgical ctDNA for Prognostic Prediction in Hepatocellular Carcinoma.

Detecting residual disease is a critical clinical requirement in the peri-surgical management of patients with resectable hepatocellular carcinoma (HCC). Previous studies focused on specific genomic regions exhibiting limited sensitivity and failed to meet the minimal residual disease (MRD) testing threshold. We introduce a next-generation sequencing (NGS) based assay, informed by baseline samples, facilitating MRD detection in hepatectomized HCC patients and offering prognostic predictions. This study involved 88 HCC patients who underwent surgical resections January 2016 to May 2016 in Zhongshan Hospital Fudan University. Tumor and normal tissue samples were collected during surgery, while plasma samples were obtained both before surgery and up to seven days post-surgery. Using an NGS-based personalized circulating tumor DNA assay, we analyzed MRD in both pre-surgical and post-surgical blood samples and the correlation with prognosis. With a median follow-up period of 80.7 months, our findings demonstrated significant correlations between pre-surgical ctDNA tumor fractions, post-surgical plasma MRD status, and both recurrence-free survival (RFS) and overall survival (OS). Post-surgical MRD status emerged as the most significant risk factor for cancer recurrence (HR=2.17, 95% CI: 1.09-4.30, p=0.027) compared to other clinical characteristics in multivariate Cox regression analysis. Notably, MRD status showed potential as a prognostic indicator among clinically low-recurrent-risk patients, such as those with BCLC stage 0-A, CNLC stage I-II. Evaluating personalized MRD provided crucial prognostic insights into RFS and OS. It efficiently identified patients at high risk of recurrence, even among those initially perceived as low-risk cases.