BACKGROUND: The three-dimensional (3D) genome architecture plays a critical role inregulating gene expression. However, the specific alterations in thisarchitecture within somatotroph tumors and their implications for gene expression remain largely unexplored.
METHODS: We employed Hi-C and RNA-seq analyses to compare the 3D genomic structures of somatotroph tumors with normal pituitary tissue. This comprehensive approachenabled the characterization of A/B compartments, topologically associateddomains (TADs), and chromatin loops, integrating these with gene expression patterns.
RESULTS: We observed a decrease in both the frequency of chromosomal interactions andthe size of TADs in tumor tissue compared to normal tissue. Conversely, the number of TADs and chromatin loops was found to be increased in tumors. Integrated analysis of Hi-C and RNA-seq data demonstrated that changes inhigher-order chromat in structure were associated with alterations in gene expression. Specifically, genes in A compartments showed higher density and increased expression relative to those in B compartments. Moreover, the weakand enhanced insulation boundaries were identified, and the associated genes were enriched in the Wnt/β-Catenin signaling pathway. We identified the gainedand lost loops in tumor and integrated these differences with transcriptional changes to examine the functional relevance of the identified loops. Notably, we observed an enhanced insulation boundary and a greater number of loops in the TCF7L2 gene region within tumors, which was accompanied by an upregulation of TCF7L2 expression. Subsequently, TCF7L2 expression was confirmed through qRT-PCR, and upregulated TCF7L2 prompted cell proliferation and growth hormone (GH) secretion in vitro.
CONCLUSIONS: Our results provide comprehensive 3D chromatin architecture maps of somatotroph tumors and offer a valuable resource for furthering the understanding of the underlying biology and mechanisms of gene expression regulation.

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Brainbow is a genetic cell-labeling technique that allows random colorization of multiple cells and real-time visualization of cell fate within a tissue, providing valuable insights into understanding complex biological processes. However, fluorescent proteins (FPs) in Brainbow have distinct excitation spectra with peak difference greater than 35 nm, which requires sequential imaging under multiple excitations and thus leads to long acquisition times. In addition, they are not easily used together with other fluorophores due to severe spectral bleed-through. Here, we report the development of a single-wavelength excitable Brainbow, UFObow, incorporating three newly developed blue-excitable FPs. We have demonstrated that UFObow enables not only tracking the growth dynamics of tumor cells in vivo but also mapping spatial distribution of immune cells within a sub-cubic centimeter tissue, revealing cell heterogeneity. This provides a powerful means to explore complex biology in a simultaneous imaging manner at a single-cell resolution in organs or in vivo.

5-Methylcytosine (m5C), an abundant RNA modification, plays a crucial role in regulating RNA fate and gene expression. While recent progress has been made in understanding the biological roles of m5C, the inability to introduce m5C at specific sites within transcripts has hindered efforts to elucidate direct links between specific m5C and phenotypic outcomes. Here, we developed a CRISPR-Cas13d-based tool, named reengineered m5C modification system (termed \'RCMS\'), for targeted m5C methylation and demethylation in specific transcripts. The RCMS editors consist of a nuclear-localized dCasRx conjugated to either a methyltransferase, NSUN2/NSUN6, or a demethylase, the catalytic domain of mouse Tet2 (ten-eleven translocation 2), enabling the manipulation of methylation events at precise m5C sites. We demonstrate that the RCMS editors can direct site-specific m5C incorporation and demethylation. Furthermore, we confirm their effectiveness in modulating m5C levels within transfer RNAs and their ability to induce changes in transcript abundance and cell proliferation through m5C-mediated mechanisms. These findings collectively establish RCMS editors as a focused epitranscriptome engineering tool, facilitating the identification of individual m5C alterations and their consequential effects.

Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

Plasmids are the most commonly used gene carriers in the field of gene synthesis and sequencing. However, the main problems faced by traditional plasmid DNA extraction technology are low extraction throughput and high production cost, so they cannot meet the growing demand. In this study, a double-magnetic-bead method (DMBM) for plasmid extraction was developed based on the principle of plasmid extraction. The effects of the input of magnetic beads, the size of plasmid DNA fragments, and the volume of bacterial on plasmid DNA extraction were explored. In addition, the quality, throughput, and cost of plasmid DNA extraction were also compared between this technique and the commercial plasmid DNA extraction kits. The results showed that the DMBM can meet the needs of extracting plasmid DNA with different cell densities and fragment lengths. Moreover, the sensitivity and quality of plasmid extraction by the DMBM method were both superior to those of the centrifugal adsorption column method. In addition, this technique could be applied on a 96-channel automated nucleic acid extractor, resulting in higher purity of the extracted plasmid DNA, 80% reduction in extraction time, and 57.1% reduction in cost. It also reduces manual operations, achieving high-throughput and low-cost plasmid DNA extraction, thus may facilitate gene synthesis and sequencing.

Nowadays, engineered Komagataella phaffii plays an important role in the biosynthesis of small molecule metabolites and protein products, showing great potential and value in industrial productions. With the development and application of new editing tools such as CRISPR/Cas9, it has become possible to engineer K. phaffii into a cell factory with high polygenic efficiency. Here, the genetic manipulation techniques and objectives for engineering K. phaffii are first summarized. Secondly, the applications of engineered K. phaffii as a cell factory are introduced. Meanwhile, the advantages as well as disadvantages of using engineered K. phaffii as a cell factory are discussed and future engineering directions are prospected. This review aims to provide a reference for further engineering K. phaffii cell factory, which is supposed to facilitate its application in bioindustry.

Although fusions between the centromeres of different human chromosomes have been observed cytologically in cancer cells, since the centromeres are long arrays of satellite sequences, the details of these fusions have been difficult to investigate. We developed methods of detecting recombination within the centromeres of the yeast Saccharomyces cerevisiae (intercentromere recombination). These events occur at similar rates (about 10-8/cell division) between two active or two inactive centromeres. We mapped the breakpoints of most of the recombination events to a region of 43 base pairs of uninterrupted homology between the two centromeres. By whole-genome DNA sequencing, we showed that most (>90%) of the events occur by non-reciprocal recombination (gene conversion/break-induced replication). We also found that intercentromere recombination can involve non-homologous chromosome, generating whole-arm translocations. In addition, intercentromere recombination is associated with very frequent chromosome missegregation. These observations support the conclusion that intercentromere recombination generally has negative genetic consequences.

OBJECTIVE: To delineate the origin and content of a mosaicism small supernumerary marker chromosome (sSMC) in a fetus with combined chromosomal karyotyping, chromosomal microarray analysis (CMA) and fluorescence in situ hybridization (FISH).
METHODS: The fetus of a 31-year-old pregnant woman who had presented at the Maternal and Child Health Care Hospital of Longhua District of Shenzhen City in 2022 was selected as the study subject. Non-invasive prenatal testing suggested that the fetus has harbored a 8.75 Mb duplication in 4q12q13.1. With informed consent, amniotic fluid and peripheral blood samples were taken from the couple for chromosomal karyotyping analysis. The origin and content of a sSMC was identified by CMA, and its proportion in amniotic fluid was determined with a FISH assay.
RESULTS: The karyotypes of the pregnant woman, her husband and the fetus were respectively determined as 46,XX, 46,XY,inv(9)(p12q12), and 47,XY,inv(9)(p12q12)pat,+mar[75]/ 46,XY,inv(9)(p12q12)pat[25]. CMA test of the amniotic fluid sample was arr[hg19]4p11q13.1(48978053_63145931)×3, which revealed no mosaicism. However, FISH analysis showed that 59% of interphase cells from the cultured amniotic fluid sample had contained three signals for the centromere of chromosome 4, whilst 65% of interphase cells from the re-sampled amniotic fluid had three such signals, which confirmed the existence of trisomy 8 mosaicism.
CONCLUSIONS: Chromosomal structural abnormality combined with mosaicism can be delineated with combined chromosomal karyotyping and molecular techniques such as FISH and CMA, which has enabled more accurate counseling for the family.