Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data.

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.

What is the role of microbial biotechnology and genetic engineering in medicine?

Microbial products are essential for developing various therapeutic agents, including antibiotics, anticancer drugs, vaccines, and therapeutic enzymes. Genetic engineering techniques, functional genomics, and synthetic biology unlock previously uncharacterized natural products. This review highlights major advances in microbial biotechnology, focusing on gene-based technologies for medical applications.

UFObow: A single-wavelength excitable Brainbow for simultaneous multicolor ex-vivo and in-vivo imaging of mammalian cells.

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.

Genome-scale requirements for dynein-based transport revealed by a high-content arrayed CRISPR screen.

The microtubule motor dynein plays a key role in cellular organization. However, little is known about how dynein's biosynthesis, assembly, and functional diversity are orchestrated. To address this issue, we have conducted an arrayed CRISPR loss-of-function screen in human cells using the distribution of dynein-tethered peroxisomes and early endosomes as readouts. From a genome-wide gRNA library, 195 validated hits were recovered and parsed into those impacting multiple dynein cargoes and those whose effects are restricted to a subset of cargoes. Clustering of high-dimensional phenotypic fingerprints revealed co-functional proteins involved in many cellular processes, including several candidate novel regulators of core dynein functions. Further analysis of one of these factors, the RNA-binding protein SUGP1, indicates that it promotes cargo trafficking by sustaining functional expression of the dynein activator LIS1. Our data represent a rich source of new hypotheses for investigating microtubule-based transport, as well as several other aspects of cellular organization captured by our high-content imaging.

Paul Berg and the origins of recombinant DNA.

In fall 1972, Paul Berg's laboratory published articles in PNAS describing two methods for constructing recombinant DNAs in vitro. He received half of the 1980 Nobel Prize in Chemistry for this landmark accomplishment. Here, we describe how this discovery came about, revolutionizing both biological research and the pharmaceutical industry.

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.

BACKGROUND: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. RESULTS: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. CONCLUSIONS: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

ATR blocks telomerase from converting DNA breaks into telomeres.

Telomerase, the enzyme that maintains telomeres at natural chromosome ends, should be repressed at double-strand breaks (DSBs), where neotelomere formation can cause terminal truncations. We developed an assay to detect neotelomere formation at Cas9- or I-SceI-induced DSBs in human cells. Telomerase added telomeric repeats to DSBs, leading to interstitial telomeric repeat insertions or the formation of functional neotelomeres accompanied by terminal deletions. The threat that telomerase poses to genome integrity was minimized by ataxia telangiectasia and Rad3-related (ATR) kinase signaling, which inhibited telomerase at resected DSBs. In addition to acting at resected DSBs, telomerase used the extruded strand in the Cas9 enzyme-product complex as a primer for neotelomere formation. We propose that although neotelomere formation is detrimental in normal human cells, it may allow cancer cells to escape from breakage-fusion-bridge cycles.

Downregulation of Hypoxia Inducible Factor-1α in Primary Human Natural Killer Cells Using Small Interfering RNA Delivery with ExPERT ATx by MaxCyte.

Natural killer (NK) cells are innate cytokine-producing and cytolytic effector lymphocytes. Their function is responsive to environmental factors, e.g., hypoxia, a frequent feature of inflamed tissues. Such responses require that the NK cells up-regulate HIF-1α (hypoxia inducible factor-1α), the major mediator of cellular responses to hypoxia that affects cell survival as well as immune responses. Thus, a major approach to the study of NK cell effector function under hypoxic conditions involves the ability to regulate HIF-1α levels in primary human NK cells. One difficulty with this approach, however, is that NK cells are difficult-to-transfect cells and common transfection methods, including electroporation or lipofection, suffer from variable transfection efficiency and cell viability. Moreover, the detection of HIF-1α is technically challenging because of the rapid degradation of the protein under normoxic conditions. Here, using the commercially available ExPERT ATx by MaxCyte, we report a workflow for the reliable delivery of small interfering RNA (siRNA) for targeting HIF-1α expression in primary human NK cells. We further provide a protocol for the detection of HIF-1α by immunoblot analysis demonstrating its efficient downregulation by siRNA. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Isolation of natural killer cells from human peripheral blood mononuclear cells Basic Protocol 2: Delivery of non-coding small interfering RNA and HIF-1α targeting siRNA into natural killer cells using ExPERT ATx Basic Protocol 3: Assessing the downregulation of HIF-1α protein using immunoblot analysis Support Protocol 1: Exemplary assessment of transfection efficiency using fluorescently labeled non-targeting siRNA Support Protocol 2: Exemplary assessment of NK cell viability 20 hr post-transfection Support Protocol 3: Exemplary assessment of HIF-1α knockdown using immunoblot analysis.

Cross-contamination of CRISPR guides and other unrelated nucleotide sequences among commercial oligonucleotides.

Custom oligonucleotides (oligos) are widely used reagents in biomedical research. Some common applications of oligos include polymerase chain reaction (PCR), sequencing, hybridization, microarray, and library construction. The reliability of oligos in such applications depends on their purity and specificity. Here, we report that commercially available oligos are frequently contaminated with nonspecific sequences (i.e. other unrelated oligonucleotides). Most of the oligos that we designed to amplify clustered regularly interspersed palindromic repeats (CRISPR) guide sequences contained nonspecific CRISPR guides. These contaminants were detected in research-grade oligos procured from eight commercial oligo-suppliers located in three different geographic regions of the world. Deep sequencing of some of the oligos revealed a variety of contaminants. Given the wide range of applications of oligos, the impact of oligo cross-contamination varies greatly depending on the field and the experimental method. Incorporating appropriate control experiments in research design can help ensure that the quality of oligo reagents meets the intended purpose. This can also minimize risk depending on the purposes for which the oligos are used.

Global identification of mRNA-interacting circular RNAs by CLiPPR-Seq.

Although the functional role of circular RNA (circRNA) interaction with microRNAs and proteins has been studied extensively, circRNA interactions with the protein-coding mRNAs in intact cells remain largely unknown. Here, by employing AMT-mediated proximity ligation of RNA-RNA duplexes followed by circRNA enrichment and deep sequencing, we report a novel Cross-Linking Poly(A) Pulldown RNase R Sequencing (CLiPPR-seq) technology which identified hundreds of mRNA-interacting circRNAs in three different cell types, including ßTC6, C2C12 and HeLa cells. Furthermore, CLiPP-seq without RNase R treatment was also performed to identify the mRNA expression in these cells. BLAST analysis of circRNAs in CLiPPR-seq sample with the mRNAs in CLiPP-seq samples determined their potential complementary sequences for circRNA-mRNA interaction. Pulldown of circRNAs and poly(A) RNAs confirmed the direct interaction of circRNAs with target mRNAs. Silencing of mRNA-interacting circRNAs led to the altered expression of target mRNAs in ßTC6 cells, suggesting the role of direct interaction of circRNAs with mRNAs in gene expression regulation. CLiPPR-seq thus represents a novel method for illuminating the myriad of uncharacterized circRNA-mRNA hybrids that may regulate gene expression.

Functional identification of cis-regulatory long noncoding RNAs at controlled false discovery rates.

A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such 'cis-lncRNAs' are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistor's simple and conservative statistical models are compatible with functionally defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. We detect cis-activity in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target genes and are weakly enriched with enhancer elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance.

Programmable RNA 5-methylcytosine (m5C) modification of cellular RNAs by dCasRx conjugated methyltransferase and demethylase.

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.

A system of reporters for comparative investigation of EJC-independent and EJC-enhanced nonsense-mediated mRNA decay.

Nonsense-mediated mRNA decay (NMD) is a network of pathways that degrades transcripts that undergo premature translation termination. In mammals, NMD can be divided into the exon junction complex (EJC)-enhanced and EJC-independent branches. Fluorescence- and luminescence-based reporters have long been effective tools to investigate NMD, yet existing reporters largely focus on the EJC-enhanced pathway. Here, we present a system of reporters for comparative studies of EJC-independent and EJC-enhanced NMD. This system also enables the study of NMD-associated outcomes such as premature termination codon (PTC) readthrough and truncated protein degradation. These reporters are compatible with fluorescence or luminescence-based readouts via transient transfection or stable integration. Using this reporter system, we show that EJC-enhanced NMD RNA levels are reduced by 2- or 9-fold and protein levels are reduced by 7- or 12-fold compared to EJC-independent NMD, depending on the reporter gene used. Additionally, the extent of readthrough induced by G418 and an NMD inhibitor (SMG1i), alone and in combination, varies across NMD substrates. When combined, G418 and SMG1i increase readthrough product levels in an additive manner for EJC-independent reporters, while EJC-enhanced reporters show a synergistic effect. We present these reporters as a valuable toolkit to deepen our understanding of NMD and its associated mechanisms.

Optimizing sequence design strategies for perturbation MPRAs: a computational evaluation framework.

The advent of perturbation-based massively parallel reporter assays (MPRAs) technique has facilitated the delineation of the roles of non-coding regulatory elements in orchestrating gene expression. However, computational efforts remain scant to evaluate and establish guidelines for sequence design strategies for perturbation MPRAs. In this study, we propose a framework for evaluating and comparing various perturbation strategies for MPRA experiments. Within this framework, we benchmark three different perturbation approaches from the perspectives of alteration in motif-based profiles, consistency of MPRA outputs, and robustness of models that predict the activities of putative regulatory motifs. While our analyses show very similar results across multiple benchmarking metrics, the predictive modeling for the approach involving random nucleotide shuffling shows significant robustness compared with the other two approaches. Thus, we recommend designing sequences by randomly shuffling the nucleotides of the perturbed site in perturbation-MPRA, followed by a coherence check to prevent the introduction of other variations of the target motifs. In summary, our evaluation framework and the benchmarking findings create a resource of computational pipelines and highlight the potential of perturbation-MPRA in predicting non-coding regulatory activities.

MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops.

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.

Gene panel selection for targeted spatial transcriptomics.

Targeted spatial transcriptomics hold particular promise in analyzing complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is their reliance on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method performing gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

Splitting the yeast centromere by recombination.

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.

CRISPR-dCas13a system for programmable small RNAs and polycistronic mRNA repression in bacteria.

Bacterial small RNAs (sRNAs) function in post-transcriptional regulatory responses to environmental changes. However, the lack of eukaryotic RNA interference-like machinery in bacteria has limited the systematic engineering of RNA repression. Here, we report the development of clustered regularly interspaced short palindromic repeats (CRISPR)-guided dead CRIPSR-associated protein 13a (dCas13a) ribonucleoprotein that utilizes programmable CRISPR RNAs (crRNAs) to repress trans-acting and cis-acting sRNA as the target, altering regulatory mechanisms and stress-related phenotypes. In addition, we implemented a modular loop engineering of the crRNA to promote modular repression of the target gene with 92% knockdown efficiency and a single base-pair mismatch specificity. With the engineered crRNAs, we achieved targetable single-gene repression in the polycistronic operon. For metabolic application, 102 crRNAs were constructed in the biofoundry and used for screening novel knockdown sRNA targets to improve lycopene (colored antioxidant) production in Escherichia coli. The CRISPR-dCas13a system will assist as a valuable systematic tool for the discovery of novel sRNAs and the fine-tuning of bacterial RNA repression in both scientific and industrial applications.

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection.

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.