RNA virus interference via CRISPR/Cas13a system in plants

scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.

3D clustering of gene expression data from systemic autoinflammatory diseases using self-organizing maps (Clust3D)

Background and objective

Systemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms have been used to analyze SAID datasets, aiding in the discovery of novel biomarkers, there is a growing recognition of the importance of SAID timeseries clustering, as it can capture the temporal dynamics of gene expression patterns.

Methodology

This paper proposes a novel clustering methodology to efficiently associate three-dimensional data. The algorithm utilizes competitive learning to create a self-organizing neural network and adjust neuron positions in time-dependent and high dimensional feature space in order to assign them as clustering centers. The quantitative evaluation of the clustering was based on well-known clustering indices. Furthermore, a differential expression analysis and classification pipeline was employed to assess the capability of the proposed methodology to extract more accurate pathway-specific genes from its clusters. For that, a comparative analysis was also conducted against a heuristic timeseries clustering method.

Results

The proposed methodology achieved better overall clustering indices scores and classification metrics using genes derived from its clusters. Notable cases include a threefold increase in the Calinski-Harabasz clustering index, a twofold improvement in the Davies-Bouldin clustering index and a ∼ 60 % increase in the classification specificity score.

Conclusion

A novel clustering methodology was developed and applied on several gene expression timeseries datasets from systemic autoinflammatory diseases, and its ability to efficiently produce well separated clusters compared to existing heuristic methods was demonstrated.

Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics

Spatial transcriptomics techniques, while measuring gene expression, retain spatial location information, aiding in situ studies of organismal tissue architecture and the progression of pathological processes. These techniques generate vast amounts of omics data, necessitating the development of computational methods to reveal the underlying tissue microenvironment heterogeneity. The main directions in spatial transcriptomics data analysis are spatial domain detection and spatial deconvolution, which can identify spatial functional regions and parse the distribution of cell types in spatial transcriptomics data by integrating single-cell transcriptomics data. In these two research directions, many computational methods have been successively proposed. This article will categorize them into three types: machine learning-based methods, probabilistic models-based methods, and deep learning-based methods. It will list and discuss the representative algorithms of each type along with their advantages and disadvantages and describe the datasets and evaluation metrics used to assess these computational methods, facilitating researchers in selecting suitable computational methods according to their research needs. Finally, combining the latest technological developments and the advantages and disadvantages of current algorithms, this article will look forward to the future directions of computational method development.

AFSC: A self-supervised augmentation-free spatial clustering method based on contrastive learning for identifying spatial domains

Recent research in spatial transcriptomics allows researchers to analyze gene expression without losing spatial information. Spatial information can assist in cell communication, identification of new cell subtypes, which provides important research methods for multiple fields such as microenvironment interactions and pathological processes of diseases. Identifying spatial domains is an important step in spatial transcriptomics analysis, and improving spatial clustering methods can benefit for identifying spatial domains. In addition to eliminating noise in original gene expression, how to use spatial information to assist clustering has also become a new problem. A variety of calculating methods have been applied to spatial clustering, including contrastive learning methods. However, existing spatial clustering methods based on contrastive learning use data augmentation to generate positive and negative pairs, which will inevitably destroy the biological meaning of the data. We propose a new self-supervised spatial clustering method based on contrastive learning, Augmentation-Free Spatial Clustering (AFSC), which integrates spatial information and gene expression to learn latent representations. We construct a contrastive learning module without negative pairs or data augmentation by designing Teacher and Student Encoder. We also design an unsupervised clustering module to make clustering and contrastive learning be trained together. Experiments on multiple spatial transcriptomics datasets at different resolutions demonstrate that our method performs well in self-supervised spatial clustering tasks. Furthermore, the learned representations can be used for various downstream tasks including visualization and trajectory inference.

CRISPR/Cas9 opens new horizon of crop improvement under stress condition

Plants are exposed to a myriad of stresses, stemming from abiotic and biotic sources, significantly threatening agricultural productivity. The low crop yield, coupled with the global burden of population has resulted in the scarcity of quality food, exacerbating socio-economic issues like poverty, hunger, and malnutrition. Conventional breeding methods for the generation of stress-tolerant plants are time-consuming, limit genetic diversity, and are not sustainable for the consistent production of high-yielding crops. In recent years, the use of high-throughput, genome editing (GE) technique has revolutionized the crop-improvement paradigm, ushering greater prospects for agricultural progress. Among these tools, the Clustered regularly interspaced short palindromic repeat (CRISPR), and its associated nuclease protein Cas9, have appeared as a ground-breaking technology, allowing precise knockout (KO), upregulation, and downregulation of target gene expression. Apart from its high efficacy and speed, this programmable nuclease offers exceptional specificity with minimal off-target effects. Here in, we aim to review the latest findings on the application of the CRISPR/Cas9 genome editing tool for generating resilience in plants against environmental stresses.

Host and antibiotic jointly select for greater virulence in Staphylococcus aureus

Widespread antibiotic usage has resulted in the rapid evolution of drug-resistant bacterial pathogens and poses significant threats to public health. Resolving how pathogens respond to antibiotics under different contexts is critical for understanding disease emergence and evolution going forward. The impact of antibiotics has been demonstrated most directly through in vitro pathogen passaging experiments. Independent from antibiotic selection, interactions with hosts have also altered the evolutionary trajectories and fitness landscapes of pathogens, shaping infectious disease outcomes. However, it is unclear how interactions between hosts and antibiotics impact the evolution of pathogen virulence. Here, we evolved and re-sequenced Staphylococcus aureus, a major bacterial pathogen, varying exposure to host and antibiotics to tease apart the contributions of these selective pressures on pathogen adaptation. After 12 passages, S. aureus evolving in Caenorhabditis elegans nematodes exposed to a sub-minimum inhibitory concentration of antibiotic (oxacillin) became highly virulent, regardless of whether the ancestral pathogen was methicillin-resistant (MRSA) or methicillin-sensitive (MSSA). Host and antibiotic exposure selected for reduced drug susceptibility in MSSA lineages while increasing MRSA total growth outside hosts. We identified mutations in genes involved in complex regulatory networks linking virulence and metabolism, including codY , agr , and gdpP , suggesting that rapid adaptation to infect hosts may have pleiotropic effects. In particular, MSSA populations under selection from host and antibiotic accumulated mutations in the global regulator gene codY , which controls biofilm formation in S. aureus. These populations had indeed evolved more robust biofilms-a trait linked to both virulence and antibiotic resistance-suggesting evolution of one trait can confer multiple adaptive benefits. Despite evolving in similar environments, MRSA and MSSA populations proceeded on divergent evolutionary paths, with MSSA populations exhibiting more similarities across replicate populations. Our results underscore the importance of considering multiple and concurrent selective pressures as drivers of pervasive pathogen traits.

Prokrustean Graph: A substring index for rapid k-mer size analysis

Despite the widespread adoption of k -mer-based methods in bioinformatics, understanding the influence of k -mer sizes remains a persistent challenge. Selecting an optimal k -mer size or employing multiple k -mer sizes is often arbitrary, application-specific, and fraught with computational complexities. Typically, the influence of k -mer size is obscured by the outputs of complex bioinformatics tasks, such as genome analysis, comparison, assembly, alignment, and error correction. However, it is frequently overlooked that every method is built above a well-defined k -mer-based object like Jaccard Similarity, de Bruijn graphs, k -mer spectra, and Bray-Curtis Dissimilarity. Despite these objects offering a clearer perspective on the role of k -mer sizes, the dynamics of k -mer-based objects with respect to k -mer sizes remain surprisingly elusive. This paper introduces a computational framework that generalizes the transition of k -mer-based objects across k -mer sizes, utilizing a novel substring index, the Pro k rustean graph. The primary contribution of this framework is to compute quantities associated with k -mer-based objects for all k -mer sizes, where the computational complexity depends solely on the number of maximal repeats and is independent of the range of k -mer sizes. For example, counting vertices of compacted de Bruijn graphs for k = 1, …, 100 can be accomplished in mere seconds with our substring index constructed on a gigabase-sized read set. Additionally, we derive a space-efficient algorithm to extract the Pro k rustean graph from the Burrows-Wheeler Transform. It becomes evident that modern substring indices, mostly based on longest common prefixes of suffix arrays, inherently face difficulties at exploring varying k -mer sizes due to their limitations at grouping co-occurring substrings. We have implemented four applications that utilize quantities critical in modern pangenomics and metagenomics. The code for these applications and the construction algorithm is available at https://github.com/KoslickiLab/prokrustean .

scVGATAE: A Variational Graph Attentional Autoencoder Model for Clustering Single-Cell RNA-seq Data

Single-cell RNA sequencing (scRNA-seq) is now a successful technology for identifying cell heterogeneity, revealing new cell subpopulations, and predicting developmental trajectories. A crucial component in scRNA-seq is the precise identification of cell subsets. Although many unsupervised clustering methods have been developed for clustering cell subpopulations, the performance of these methods is prone to be affected by dropout, high dimensionality, and technical noise. Additionally, most existing methods are time-consuming and fail to fully consider the potential correlations between cells. In this paper, we propose a novel unsupervised clustering method called scVGATAE (Single-cell Variational Graph Attention Autoencoder) for scRNA-seq data. This method constructs a reliable cell graph through network denoising, utilizes a novel variational graph autoencoder model integrated with graph attention networks to aggregate neighbor information and learn the distribution of the low-dimensional representations of cells, and adaptively determines the model training iterations for various datasets. Finally, the obtained low-dimensional representations of cells are clustered using kmeans. Experiments on nine public datasets show that scVGATAE outperforms classical and state-of-the-art clustering methods.

Predicting Differentially Methylated Cytosines in TET and DNMT3 Knockout Mutants via a Large Language Model

DNA cytosine methylation is an epigenetic marker which regulates many cellular processes. Mammalian genomes typically maintain consistent methylation patterns over time, except in specific regulatory regions like promoters and certain types of enhancers. The dynamics of DNA methylation is controlled by a complex cellular machinery, in which the enzymes DNMT3 and TET play a major role. This study explores the identification of differentially methylated cytosines (DMCs) in TET and DNMT3 knockout mutants in mice and human embryonic stem cells. We investigate (i) whether a large language model can be trained to recognize DMCs in human and mouse from the sequence surrounding the cytosine of interest, (ii) whether a classifier trained on human knockout data can predict DMCs in the mouse genome (and vice versa), (iii) whether a classifier trained on DNMT3 knockout can predict DMCs for TET knockout (and vice versa). Our study identifies statistically significant motifs associated with the prediction of DMCs each mutant, casting a new light on the understanding of DNA methylation dynamics in stem cells. Our software tool is available at https://github.com/ucrbioinfo/dmc_prediction .

NF-κB-activated oncogene inhibition strategy for cancer gene therapy

NF-κB is a promising target for cancer treatment because of its overactivation in almost all cancers but countless NF-κB inhibitors rarely became clinical drugs due to side effects. In contrast to traditional cancer treatments aimed at inhibiting NF-κB activity, this study develop a novel approach termed HOPE, which focuses on activating the exogenous effector gene CRISPR-Cas13a within cancer cells, achieved by utilizing the NF-κB-specific promoter DMP previously constructed, then targets and suppresses the expression of oncogenes TERT, PLK1, KRAS and MYC at mRNA level. We evaluated the antitumour effects of HOPE in various cultured cells and confirmed it could induce obvious the death of cancer cells without affecting normal cells. By packaging HOPE into adeno-associated virus (AAV) and intravenously injected it to treat mice that were subcutaneously transplanted with colorectal cancer. This validated that rAAV-HOPE could significantly inhibit tumour growth without side effects. Based on the scRNA-seq data, we observed that HOPE could activate the immune system and decrease the proportion of cancer cells, particularly reducing the stemness of cancer cells. This study elucidates an important role of HOPE in inhibiting cancer cell growth both in vitro and in vivo, additionally provides a novel therapeutic technology for cancer gene therapy.

Identification of Hypoxia-ALCAMhigh Macrophage- Exhausted T Cell Axis in Tumor Microenvironment Remodeling for Immunotherapy Resistance

Although hypoxia is known to be associated with immune resistance, the adaptability to hypoxia by different cell populations in the tumor microenvironment and the underlying mechanisms remain elusive. This knowledge gap has hindered the development of therapeutic strategies to overcome tumor immune resistance induced by hypoxia. Here, bulk, single-cell, and spatial transcriptomics are integrated to characterize hypoxia associated with immune escape during carcinogenesis and reveal a hypoxia-based intercellular communication hub consisting of malignant cells, ALCAMhigh macrophages, and exhausted CD8+ T cells around the tumor boundary. A hypoxic microenvironment promotes binding of HIF-1α complex is demonstrated to the ALCAM promoter therefore increasing its expression in macrophages, and the ALCAMhigh macrophages co-localize with exhausted CD8+ T cells in the tumor spatial microenvironment and promote T cell exhaustion. Preclinically, HIF-1ɑ inhibition reduces ALCAM expression in macrophages and exhausted CD8+ T cells and potentiates T cell antitumor function to enhance immunotherapy efficacy. This study reveals the systematic landscape of hypoxia at single-cell resolution and spatial architecture and highlights the effect of hypoxia on immunotherapy resistance through the ALCAMhigh macrophage-exhausted T cell axis, providing a novel immunotherapeutic strategy to overcome hypoxia-induced resistance in cancers.

Advances in plant pathogen detection: integrating recombinase polymerase amplification with CRISPR/Cas systems

Plant pathogens are causing substantial economic losses and thus became a significant threat to global agriculture. Effective and timely detection methods are prerequisite for combating the damages caused by the plant pathogens. In the realm of plant pathogen detection, the isothermal amplification techniques, e.g., recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP), have emerged as a fast, precise, and most sensitive alternative to conventional PCR but they often comprise high rates of non-specific amplification and operational complexity. In recent advancements, clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated nuclease Cas systems, particularly Cas12, have emerged as powerful tools for highly sensitive, specific, and rapid pathogen detection. Exploiting the collateral activities of Cas12, which selectively cleaves single-stranded DNA (ssDNA), novel detection platforms have been developed. The mechanism employs the formation of a triple complex molecule comprising guide RNA, Cas12 enzyme, and the substrate target nucleotide sequence. Upon recognition of the target, Cas12 indiscriminately cleaves the DNA strand, leading to the release of fluorescence from the cleaved ssDNA reporter. Integration of isothermal amplification methods with CRISPR/Cas12 enables one-step detection assays, facilitating rapid pathogen identification within 30 min at a single temperature. This integrated RPA-CRISPR/Cas12a approach eliminates the need for RNA extraction and cDNA conversion, allowing direct use of crude plant sap as a template. With an affordable fluorescence visualization system, this portable method achieves 100-fold greater sensitivity than conventional techniques. This review summarizes recent advances in RPA-CRISPR/Cas12a for detecting plant pathogens, covering primer design, field-level portability, and enhanced sensitivity.

Systematic Review of Genetic Substrate Reduction Therapy in Lysosomal Storage Diseases: Opportunities, Challenges and Delivery Systems

BACKGROUND

Genetic substrate reduction therapy (gSRT), which involves the use of nucleic acids to downregulate the genes involved in the biosynthesis of storage substances, has been investigated in the treatment of lysosomal storage diseases (LSDs).

OBJECTIVE

To analyze the application of gSRT to the treatment of LSDs, identifying the silencing tools and delivery systems used, and the main challenges for its development and clinical translation, highlighting the contribution of nanotechnology to overcome them.

METHODS

A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines was performed. PubMed, Scopus, and Web of Science databases were used for searching terms related to LSDs and gene-silencing strategies and tools.

RESULTS

Fabry, Gaucher, and Pompe diseases and mucopolysaccharidoses I and III are the only LSDs for which gSRT has been studied, siRNA and lipid nanoparticles being the silencing strategy and the delivery system most frequently employed, respectively. Only in one recently published study was CRISPR/Cas9 applied to treat Fabry disease. Specific tissue targeting, availability of relevant cell and animal LSD models, and the rare disease condition are the main challenges with gSRT for the treatment of these diseases. Out of the 11 studies identified, only two gSRT studies were evaluated in animal models.

CONCLUSIONS

Nucleic acid therapies are expanding the clinical tools and therapies currently available for LSDs. Recent advances in CRISPR/Cas9 technology and the growing impact of nanotechnology are expected to boost the clinical translation of gSRT in the near future, and not only for LSDs.

Resistance of the CRISPR-Cas13a Gene-Editing System to Potato Spindle Tuber Viroid Infection in Tomato and Nicotiana benthamiana

Gene-editing technology, specifically the CRISPR-Cas13a system, has shown promise in breeding plants resistant to RNA viruses. This system targets RNA and, theoretically, can also combat RNA-based viroids. To test this, the CRISPR-Cas13a system was introduced into tomato plants via transient expression and into Nicotiana benthamiana through transgenic methods, using CRISPR RNAs (crRNAs) targeting the conserved regions of both sense and antisense genomes of potato spindle tuber viroid (PSTVd). In tomato plants, the expression of CRISPR-Cas13a and crRNAs substantially reduced PSTVd accumulation and alleviated disease symptoms. In transgenic N. benthamiana plants, the PSTVd levels were lower as compared to wild-type plants. Several effective crRNAs targeting the PSTVd genomic RNA were also identified. These results demonstrate that the CRISPR-Cas13a system can effectively target and combat viroid RNAs, despite their compact structures.

In vitro study of HPV18-positive cervical cancer HeLa cells based on CRISPR/Cas13a system

The E6 protein is a known oncogene in cervical cancer and plays a key role in the development and progression of cervical cancer by reducing the expression level of the tumor suppressor protein P53 and ultimately leading to enhanced cell proliferation and reduced apoptosis. Therefore, antiviral agents that inhibit the expression of E6 oncoprotein are expected to be potential therapies for human cervical cancer. Here we developed CRISPR/Cas13a: crRNA dual plasmid system and demonstrated that CRISPR/Cas13a could effectively and specifically knock down human papillomavirus 18 E6 mRNA, downregulate the expression level of E6 protein, and restore the expression of the tumor suppressor gene P53 protein, thereby inhibiting the growth of cervical cancer cells and increasing their apoptosis, the E6-2, E6-3, and E6-5 groups resulted in apoptosis rates of 25.4%, 22.4%, and 22.2% in HeLa cells. Moreover, CRISPR/Cas13a enhances the proliferation inhibition and apoptosis induction of cisplatin in cervical cancer HeLa cells. The CRISPR/Cas13a system targeting HPV E6 mRNA may be a promising therapeutic approach for the treatment of human papillomavirus-associated cervical cancer.

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs for single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. Overall, the top FMs include scGPT, Geneformer, and CellPLM by considering model performances and user accessibility among ten single-cell FMs. However, by comparing these FMs with task-specific methods, we found that single-cell FMs may not consistently excel than task-specific methods in all tasks, which challenges the necessity of developing foundation models for single-cell analysis. In addition, we evaluated the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning, to enhance the performances of single-cell FMs. Our work summarizes the current state of single-cell FMs, points to their constraints and avenues for future development, and offers a freely available evaluation pipeline to benchmark new models and improve method development.

Vaccines combining slow delivery and follicle targeting of antigens increase germinal center B cell clonal diversity and clonal expansion

Vaccines incorporating slow delivery, multivalent antigen display, or immunomodulation through adjuvants have an important role to play in shaping the humoral immune response. Here we analyzed mechanisms of action of a clinically relevant combination adjuvant strategy, where phosphoserine (pSer)-tagged immunogens bound to aluminum hydroxide (alum) adjuvant (promoting prolonged antigen delivery to draining lymph nodes) are combined with a potent saponin nanoparticle adjuvant termed SMNP (which alters lymph flow and antigen entry into lymph nodes). When employed with a stabilized HIV Env trimer antigen in mice, this combined adjuvant approach promoted substantial enhancements in germinal center (GC) and antibody responses relative to either adjuvant alone. Using scRNA-seq and scBCR-seq, we found that the alum-pSer/SMNP combination both increased the diversity of GC B cell clones and increased GC B cell clonal expansion, coincident with increases in the expression of Myc and the proportion of S-phase GC B cells. To gain insight into the source of these changes in the GC response, we analyzed antigen biodistribution and structural integrity in draining lymph nodes and found that the combination adjuvant approach, but not alum-pSer delivery or SMNP alone, promoted accumulation of highly intact antigen on follicular dendritic cells, reflecting an integration of the slow antigen delivery and altered lymph node uptake effects of these two adjuvants. These results demonstrate how adjuvants with complementary mechanisms of action impacting vaccine biodistribution and kinetics can synergize to enhance humoral immunity.

Application of CRISPR/Cas-based gene-editing for developing better banana

Banana (Musa spp.), including plantain, is one of the major staple food and cash crops grown in over 140 countries in the subtropics and tropics, with around 153 million tons annual global production, feeding about 400 million people. Despite its widespread cultivation and adaptability to diverse environments, banana production faces significant challenges from pathogens and pests that often coexist within agricultural landscapes. Recent advancements in CRISPR/Cas-based gene editing offer transformative solutions to enhance banana resilience and productivity. Researchers at IITA, Kenya, have successfully employed gene editing to confer resistance to diseases such as banana Xanthomonas wilt (BXW) by targeting susceptibility genes and banana streak virus (BSV) by disrupting viral sequences. Other breakthroughs include the development of semi-dwarf plants, and increased β-carotene content. Additionally, non-browning banana have been developed to reduce food waste, with regulatory approval in the Philippines. The future prospects of gene editing in banana looks promising with CRISPR-based gene activation (CRISPRa) and inhibition (CRISPRi) techniques offering potential for improved disease resistance. The Cas-CLOVER system provides a precise alternative to CRISPR/Cas9, demonstrating success in generating gene-edited banana mutants. Integration of precision genetics with traditional breeding, and adopting transgene-free editing strategies, will be pivotal in harnessing the full potential of gene-edited banana. The future of crop gene editing holds exciting prospects for producing banana that thrives across diverse agroecological zones and offers superior nutritional value, ultimately benefiting farmers and consumers. This article highlights the pivotal role of CRISPR/Cas technology in advancing banana resilience, yield and nutritional quality, with significant implications for global food security.

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution

We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial-indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000+ cells per run. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC has a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has an observed multiplet rate of less than 1.1% and a cell capture rate of ~50%. Although the assay can accurately profile the whole transcriptome (19,683 human or 21,400 mouse genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest - thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of 89,722 cells across multiple sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies across thousands of samples with high data quality.

The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense

A recent paper in PLOS Computational Biology (Chari and Pachter, 2023) claimed that t -SNE and UMAP embeddings of single-cell datasets fail to capture true biological structure. The authors argued that such embeddings are as arbitrary and as misleading as forcing the data into an elephant shape. Here we show that this conclusion was based on inadequate and limited metrics of embedding quality. More appropriate metrics quantifying neighborhood and class preservation reveal the elephant in the room: while t -SNE and UMAP embeddings of single-cell data do not preserve high-dimensional distances, they can nevertheless provide biologically relevant information.

Complete sequencing of ape genomes

We present haplotype-resolved reference genomes and comparative analyses of six ape species, namely: chimpanzee, bonobo, gorilla, Bornean orangutan, Sumatran orangutan, and siamang. We achieve chromosome-level contiguity with unparalleled sequence accuracy (<1 error in 500,000 base pairs), completely sequencing 215 gapless chromosomes telomere-to-telomere. We resolve challenging regions, such as the major histocompatibility complex and immunoglobulin loci, providing more in-depth evolutionary insights. Comparative analyses, including human, allow us to investigate the evolution and diversity of regions previously uncharacterized or incompletely studied without bias from mapping to the human reference. This includes newly minted gene families within lineage-specific segmental duplications, centromeric DNA, acrocentric chromosomes, and subterminal heterochromatin. This resource should serve as a definitive baseline for all future evolutionary studies of humans and our closest living ape relatives.

Compound models and Pearson residuals for single-cell RNA-seq data without UMIs

Recent work employed Pearson residuals from Poisson or negative binomial models to normalize UMI data. To extend this approach to non-UMI data, we model the additional amplification step with a compound distribution: we assume that sequenced RNA molecules follow a negative binomial distribution, and are then replicated following an amplification distribution. We show how this model leads to compound Pearson residuals, which yield meaningful gene selection and embeddings of Smart-seq2 datasets. Further, we suggest that amplification distributions across several sequencing protocols can be described by a broken power law. The resulting compound model captures previously unexplained overdispersion and zero-inflation patterns in non-UMI data.

scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning

Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.

Reprogramming human urine cells into intestinal organoids with long-term expansion ability and barrier function

Generation of intestinal organoids from human somatic cells by reprogramming would enable intestinal regeneration, disease modeling, and drug screening in a personalized pattern. Here, we report a direct reprogramming protocol for the generation of human urine cells induced intestinal organoids (U-iIOs) under a defined medium. U-iIOs expressed multiple intestinal specific genes and showed resembling gene expression profiles to primary small intestines. U-iIOs can be stably long-term expanded and further differentiated into more mature intestinal lineage cells with high expression of metallothionein and cytochrome P450 (CYP450) genes. These specific molecular features of U-iIOs differ from human pluripotent stem cells derived intestinal organoids (P-iIOs) and intestinal immortalized cell lines. Furthermore, U-iIOs exhibit intestinal barriers indicated by blocking FITC-dextran permeation and uptaking of the specific substrate rhodamine 123. Our study provides a novel platform for patient-specific intestinal organoid generation, which may lead to precision treatment of intestinal diseases and facilitate drug discovery.

An Epigenome-Wide Association Study of DNA Methylation and Proliferative Retinopathy over 28 Years in Type 1 Diabetes

Purpose

To perform a prospective epigenome-wide association study of DNA methylation (DNAm) and 28-year proliferative diabetic retinopathy (PDR) incidence in type 1 diabetes (T1D).

Design

Prospective observational cohort study.

Participants

The Pittsburgh Epidemiology of Diabetes Complications (EDC) study of childhood-onset (< 17 years) T1D.

Methods

Stereoscopic fundus photographs were taken in fields 1, 2, and 4 at baseline, 2, 4, 6, 8, 16, 23, and 28 years after DNAm measurements. The photos were graded using the modified Airlie House System. In those free of PDR at baseline (n = 265; mean T1D duration of 18 years at baseline), whole blood DNAm (EPIC array) at 683 597 CpGs was analyzed in Cox models for time to event. Associations between significant CpGs and clinical risk factors were assessed; genetic variants associated with DNAm were identified (methylation quantitative trait loci [meQTLs]). Mendelian randomization was used to examine evidence of causal associations between DNAm and PDR. Post hoc regional and functional analyses were performed.

Main Outcome Measures

Proliferative diabetic retinopathy was defined as the first instance of a grade of ≥ 60 in at least 1 eye or pan-retinal photocoagulation for PDR. Follow-up time was calculated from the study visit at which DNAm data were available (baseline) until PDR incidence or censoring (December 31, 2018 or last follow-up).

Results

PDR incidence was 53% over 28-years' follow-up. Greater DNAm of cg27512687 (KIF16B) was associated with reduced PDR incidence (P = 6.3 × 10-9; false discovery rate [FDR]: < 0.01); 113 cis-meQTLs (P < 5 × 10-8) were identified. Mendelian randomization analysis using the sentinel meQTL as the instrumental variable supported a potentially causal association between cg27512687 and PDR. Cg27512687 was also associated with lower pulse rate and albumin excretion rate and higher estimated glomerular filtration rate, but its association with PDR remained independently significant after adjustment for those factors. In regional analyses, DNAm of FUT4, FKBP1A, and RIN2 was also associated with PDR incidence.

Conclusions

DNA methylation of KIF16B, FUT4, FKBP1A, and RIN2 was associated with PDR incidence, supporting roles for epigenetic regulation of iron clearance, developmental pathways, and autophagy in PDR pathogenesis. Further study of those loci may provide insight into novel targets for interventions to prevent or delay PDR in T1D.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Programmable RNA N6-methyladenosine editing with CRISPR/dCas13a in plants

N6-methyladenonsine (m6A) is the most prevalent internal modification of messenger RNA (mRNA) and plays critical roles in mRNA processing and metabolism. However, perturbation of individual m6A modification to reveal its function and the phenotypic effects is still lacking in plants. Here, we describe the construction and characterization of programmable m6A editing tools by fusing the m6A writers, the core catalytic domain of the MTA and MTB complex, and the AlkB homologue 5 (ALKBH5) eraser, to catalytically dead Cas13a (dCas13a) to edit individual m6A sites on mRNAs. We demonstrated that our m6A editors could efficiently and specifically deposit and remove m6A modifications on specific RNA transcripts in both Nicotiana benthamiana and Arabidopsis thaliana. Moreover, we found that targeting SHORT-ROOT (SHR) transcripts with a methylation editor could significantly increase its m6A levels with limited off-target effects and promote its degradation. This leads to a boost in plant growth with enlarged leaves and roots, increased plant height, plant biomass, and total grain weight in Arabidopsis. Collectively, these findings suggest that our programmable m6A editing tools can be applied to study the functions of individual m6A modifications in plants, and may also have potential applications for future crop improvement.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

hictk: blazing fast toolkit to work with .hic and .cool files

MOTIVATION

Hi-C is gaining prominence as a method for mapping genome organization. With declining sequencing costs and a growing demand for higher-resolution data, efficient tools for processing Hi-C datasets at different resolutions are crucial. Over the past decade, the .hic and Cooler file formats have become the de-facto standard to store interaction matrices produced by Hi-C experiments in binary format. Interoperability issues make it unnecessarily difficult to convert between the two formats and to develop applications that can process each format natively.

RESULTS

We developed hictk, a toolkit that can transparently operate on .hic and .cool files with excellent performance. The toolkit is written in C++ and consists of a C++ library with Python and R bindings as well as CLI tools to perform common operations directly from the shell, including converting between .hic and .mcool formats. We benchmark the performance of hictk and compare it with other popular tools and libraries. We conclude that hictk significantly outperforms existing tools while providing the flexibility of natively working with both file formats without code duplication.

AVAILABILITY AND IMPLEMENTATION

The hictk library, Python bindings and CLI tools are released under the MIT license as a multi-platform application available at github.com/paulsengroup/hictk. Pre-built binaries for Linux and macOS are available on bioconda. Python bindings for hictk are available on GitHub at github.com/paulsengroup/hictkpy, while R bindings are available on GitHub at github.com/paulsengroup/hictkR.

Single-cell atlas of ABCA7 loss-of-function reveals impaired neuronal respiration via choline-dependent lipid imbalances

Loss-of-function (LoF) variants in the lipid transporter ABCA7 significantly increase the risk of Alzheimer's disease (odds ratio ∼2), yet the pathogenic mechanisms and the neural cell types affected by these variants remain largely unknown. Here, we performed single-nuclear RNA sequencing of 36 human post-mortem samples from the prefrontal cortex of 12 ABCA7 LoF carriers and 24 matched non-carrier control individuals. ABCA7 LoF was associated with gene expression changes in all major cell types. Excitatory neurons, which expressed the highest levels of ABCA7, showed transcriptional changes related to lipid metabolism, mitochondrial function, cell cycle-related pathways, and synaptic signaling. ABCA7 LoF-associated transcriptional changes in neurons were similarly perturbed in carriers of the common AD missense variant ABCA7 p.Ala1527Gly (n = 240 controls, 135 carriers), indicating that findings from our study may extend to large portions of the at-risk population. Consistent with ABCA7's function as a lipid exporter, lipidomic analysis of isogenic iPSC-derived neurons (iNs) revealed profound intracellular triglyceride accumulation in ABCA7 LoF, which was accompanied by a relative decrease in phosphatidylcholine abundance. Metabolomic and biochemical analyses of iNs further indicated that ABCA7 LoF was associated with disrupted mitochondrial bioenergetics that suggested impaired lipid breakdown by uncoupled respiration. Treatment of ABCA7 LoF iNs with CDP-choline (a rate-limiting precursor of phosphatidylcholine synthesis) reduced triglyceride accumulation and restored mitochondrial function, indicating that ABCA7 LoF-induced phosphatidylcholine dyshomeostasis may directly disrupt mitochondrial metabolism of lipids. Treatment with CDP-choline also rescued intracellular amyloid β -42 levels in ABCA7 LoF iNs, further suggesting a link between ABCA7 LoF metabolic disruptions in neurons and AD pathology. This study provides a detailed transcriptomic atlas of ABCA7 LoF in the human brain and mechanistically links ABCA7 LoF-induced lipid perturbations to neuronal energy dyshomeostasis. In line with a growing body of evidence, our study highlights the central role of lipid metabolism in the etiology of Alzheimer's disease.

Advancing crop disease resistance through genome editing: a promising approach for enhancing agricultural production

Modern agriculture has encountered several challenges in achieving constant yield stability especially due to disease outbreaks and lack of long-term disease-resistant crop cultivars. In the past, disease outbreaks in economically important crops had a major impact on food security and the economy. On the other hand climate-driven emergence of new pathovars or changes in their host specificity further poses a serious threat to sustainable agriculture. At present, chemical-based control strategies are frequently used to control microbial pathogens and pests, but they have detrimental impact on the environment and also resulted in the development of resistant phyto-pathogens. As a replacement, cultivating engineered disease-resistant crops can help to minimize the negative impact of regular pesticides on agriculture and the environment. Although traditional breeding and genetic engineering have been instrumental in crop disease improvement but they have certain limitations such as labour intensity, time consumption, and low efficiency. In this regard, genome editing has emerged as one of the potential tools for improving disease resistance in crops by targeting multiple traits with more accuracy and efficiency. For instance, genome editing techniques, such as CRISPR/Cas9, CRISPR/Cas13, base editing, TALENs, ZFNs, and meganucleases, have proved successful in improving disease resistance in crops through targeted mutagenesis, gene knockouts, knockdowns, modifications, and activation of target genes. CRISPR/Cas9 is unique among these techniques because of its remarkable efficacy, low risk of off-target repercussions, and ease of use. Some primary targets for developing CRISPR-mediated disease-resistant crops are host-susceptibility genes (the S gene method), resistance genes (R genes) and pathogen genetic material that prevents their development, broad-spectrum disease resistance. The use of genome editing methods has the potential to notably ameliorate crop disease resistance and transform agricultural practices in the future. This review highlights the impact of phyto-pathogens on agricultural productivity. Next, we discussed the tools for improving disease resistance while focusing on genome editing. We provided an update on the accomplishments of genome editing, and its potential to improve crop disease resistance against bacterial, fungal and viral pathogens in different crop systems. Finally, we highlighted the future challenges of genome editing in different crop systems for enhancing disease resistance.

Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses

BACKGROUND

Single cell RNA sequencing technology (scRNA-seq) has been proven useful in understanding cell-specific disease mechanisms. However, identifying genes of interest remains a key challenge. Pseudo-bulk methods that pool scRNA-seq counts in the same biological replicates have been commonly used to identify differentially expressed genes. However, such methods may lack power due to the limited sample size of scRNA-seq datasets, which can be prohibitively expensive.

RESULTS

Motivated by this, we proposed to use the Bayesian-frequentist hybrid (BFH) framework to increase the power and we showed in simulated scenario, the proposed BFH would be an optimal method when compared with other popular single cell differential expression methods if both FDR and power were considered. As an example, the method was applied to an idiopathic pulmonary fibrosis (IPF) case study.

CONCLUSION

In our IPF example, we demonstrated that with a proper informative prior, the BFH approach identified more genes of interest. Furthermore, these genes were reasonable based on the current knowledge of IPF. Thus, the BFH offers a unique and flexible framework for future scRNA-seq analyses.

The potential of genome editing to create novel alleles of resistance genes in rice

Rice, a staple food for a significant portion of the global population, faces persistent threats from various pathogens and pests, necessitating the development of resilient crop varieties. Deployment of resistance genes in rice is the best practice to manage diseases and reduce environmental damage by reducing the application of agro-chemicals. Genome editing technologies, such as CRISPR-Cas, have revolutionized the field of molecular biology, offering precise and efficient tools for targeted modifications within the rice genome. This study delves into the application of these tools to engineer novel alleles of resistance genes in rice, aiming to enhance the plant's innate ability to combat evolving threats. By harnessing the power of genome editing, researchers can introduce tailored genetic modifications that bolster the plant's defense mechanisms without compromising its essential characteristics. In this study, we synthesize recent advancements in genome editing methodologies applicable to rice and discuss the ethical considerations and regulatory frameworks surrounding the creation of genetically modified crops. Additionally, it explores potential challenges and future prospects for deploying edited rice varieties in agricultural landscapes. In summary, this study highlights the promise of genome editing in reshaping the genetic landscape of rice to confront emerging challenges, contributing to global food security and sustainable agriculture practices.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes

Motivation

Although transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions.

Results

Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data.

Availability and implementation

DifferentialRegulation is distributed as a Bioconductor R package.

Viperin-like proteins interfere with RNA viruses in plants

Plant viruses cause substantial losses in crop yield and quality; therefore, devising new, robust strategies to counter viral infections has important implications for agriculture. Virus inhibitory protein endoplasmic reticulum-associated interferon-inducible (Viperin) proteins are conserved antiviral proteins. Here, we identified a set of Viperin and Viperin-like proteins from multiple species and tested whether they could interfere with RNA viruses in planta. Our data from transient and stable overexpression of these proteins in Nicotiana benthamiana reveal varying levels of interference against the RNA viruses tobacco mosaic virus (TMV), turnip mosaic virus (TuMV), and potato virus x (PVX). Harnessing the potential of these proteins represents a novel avenue in plant antiviral approaches, offering a broader and more effective spectrum for application in plant biotechnology and agriculture. Identifying these proteins opens new avenues for engineering a broad range of resistance to protect crop plants against viral pathogens.

Structures, mechanisms and applications of RNA-centric CRISPR-Cas13

Prokaryotes are equipped with a variety of resistance strategies to survive frequent viral attacks or invading mobile genetic elements. Among these, CRISPR-Cas surveillance systems are abundant and have been studied extensively. This Review focuses on CRISPR-Cas type VI Cas13 systems that use single-subunit RNA-guided Cas endonucleases for targeting and subsequent degradation of foreign RNA, thereby providing adaptive immunity. Notably, distinct from single-subunit DNA-cleaving Cas9 and Cas12 systems, Cas13 exhibits target RNA-activated substrate RNase activity. This Review outlines structural, biochemical and cell biological studies toward elucidation of the unique structural and mechanistic principles underlying surveillance effector complex formation, precursor CRISPR RNA (pre-crRNA) processing, self-discrimination and RNA degradation in Cas13 systems as well as insights into suppression by bacteriophage-encoded anti-CRISPR proteins and regulation by endogenous accessory proteins. Owing to its programmable ability for RNA recognition and cleavage, Cas13 provides powerful RNA targeting, editing, detection and imaging platforms with emerging biotechnological and therapeutic applications.

Dysregulated alveolar epithelial cell progenitor function and identity in Hermansky-Pudlak syndrome pulmonary fibrosis

Hermansky-Pudlak syndrome (HPS) is a genetic disorder of endosomal protein trafficking associated with pulmonary fibrosis in specific subtypes, including HPS-1 and HPS-2. Single mutant HPS1 and HPS2 mice display increased fibrotic sensitivity while double mutant HPS1/2 mice exhibit spontaneous fibrosis with aging, which has been attributed to HPS mutations in alveolar epithelial type II (AT2) cells. We utilized HPS mouse models and human lung tissue to investigate mechanisms of AT2 cell dysfunction driving fibrotic remodeling in HPS. Starting at 8 weeks of age, HPS mice exhibited progressive loss of AT2 cell numbers. HPS AT2 cell was impaired ex vivo and in vivo. Incorporating AT2 cell lineage tracing in HPS mice, we observed aberrant differentiation with increased AT2-derived alveolar epithelial type I cells. Transcriptomic analysis of HPS AT2 cells revealed elevated expression of genes associated with aberrant differentiation and p53 activation. Lineage tracing and modeling studies demonstrated that HPS AT2 cells were primed to persist in a Krt8+ reprogrammed transitional state, mediated by p53 activity. Intrinsic AT2 progenitor cell dysfunction and p53 pathway dysregulation are novel mechanisms of disease in HPS-related pulmonary fibrosis, with the potential for early targeted intervention before the onset of fibrotic lung disease.

Cliffy: robust 16S rRNA classification based on a compressed LCA index

Taxonomic sequence classification is a computational problem central to the study of metagenomics and evolution. Advances in compressed indexing with the r -index enable full-text pattern matching against large sequence collections. But the data structures that link pattern sequences to their clades of origin still do not scale well to large collections. Previous work proposed the document array profiles, which use 𝒪 ( rd ) words of space where r is the number of maximal-equal letter runs in the Burrows-Wheeler transform and d is the number of distinct genomes. The linear dependence on d is limiting, since real taxonomies can easily contain 10,000s of leaves or more. We propose a method called cliff compression that reduces this size by a large factor, over 250x when indexing the SILVA 16S rRNA gene database. This method uses Θ( r log d ) words of space in expectation under a random model we propose here. We implemented these ideas in an open source tool called Cliffy that performs efficient taxonomic classification of sequencing reads with respect to a compressed taxonomic index. When applied to simulated 16S rRNA reads, Cliffy's read-level accuracy is higher than Kraken2's by 11-18%. Clade abundances are also more accurately predicted by Cliffy compared to Kraken2 and Bracken. Overall, Cliffy is a fast and space-economical extension to compressed full-text indexes, enabling them to perform fast and accurate taxonomic classification queries.

2012 ACM Subject Classification

Applied computing → Computational genomics.

CONSERVATION ASSESSMENT OF HUMAN SPLICE SITE ANNOTATION BASED ON A 470-GENOME ALIGNMENT

Despite many improvements over the years, the annotation of the human genome remains imperfect, and different annotations of the human reference genome sometimes contradict one another. The use of evolutionarily conserved sequences provides a strategy for selecting a high-confidence subset of the annotation that is more likely to be related to biological functions, and the rapidly growing number of genomes from other species increases its power. Using the latest whole genome alignment, we found that splice sites from protein-coding genes in the high-quality MANE annotation are consistently conserved across more than 400 species. We also studied splice sites from the RefSeq, GENCODE, and CHESS databases that are not present in MANE. We trained a logistic regression classifier to distinguish between the conservation exhibited by sites from MANE versus sites chosen randomly from neutrally evolving sequence. We found that splice sites classified by our model as conserved have lower SNP rates and better transcriptomic support. We then computed a subset of transcripts only using either "conserved" splice sites or ones from MANE. This subset is enriched in high-confidence transcripts of the major gene catalogs that appear to be under purifying selection and are more likely to be correct and functionally relevant.

The killifish germline regulates longevity and somatic repair in a sex-specific manner

Classical evolutionary theories propose tradeoffs between reproduction, damage repair, and lifespan. However, the specific role of the germline in shaping vertebrate aging remains largely unknown. Here, we use the turquoise killifish ( N. furzeri ) to genetically arrest germline development at discrete stages, and examine how different modes of infertility impact life-history. We first construct a comprehensive single-cell gonadal atlas, providing cell-type-specific markers for downstream phenotypic analysis. Next, we show that germline depletion - but not arresting germline differentiation - enhances damage repair in female killifish. Conversely, germline-depleted males instead showed an extension in lifespan and rejuvenated metabolic functions. Through further transcriptomic analysis, we highlight enrichment of pro-longevity pathways and genes in germline-depleted male killifish and demonstrate functional conservation of how these factors may regulate longevity in germline-depleted C. elegans . Our results therefore demonstrate that different germline manipulation paradigms can yield pronounced sexually dimorphic phenotypes, implying alternative responses to classical evolutionary tradeoffs.

Thetis cells induce food-specific Treg cell differentiation and oral tolerance

The intestinal immune system must establish tolerance to food antigens to prevent onset of allergic and inflammatory diseases. Peripherally generated regulatory T (pTreg) cells play an essential role in suppressing inflammatory responses to allergens; however, the antigen-presenting cell (APC) that instructs food-specific pTreg cells is not known. Here, we show that antigen presentation and TGF-β activation by a subset of RORγt + antigen-presenting cells (APC), Thetis cells IV (TC IV), is required for food-induced pTreg cell differentiation and oral tolerance. By contrast, antigen presentation by dendritic cells (DCs) was dispensable for pTreg induction but required for T H 1 effector responses, highlighting a division of labor between tolerogenic TCs and pro-inflammatory DCs. While antigen presentation by TCs was required for food-specific pTreg generation both in early life and adulthood, the increased abundance of TCs in the peri-weaning period was associated with a window of opportunity for enhanced pTreg differentiation. These findings establish a critical role for TCs in oral tolerance and suggest that these cells may represent a key therapeutic target for the treatment of food-associated allergic and inflammatory diseases.

Placental and Immune Cell DNA Methylation Reference Panel for Bulk Tissue Cell Composition Estimation in Epidemiological Studies

To distinguish DNA methylation (DNAm) from cell proportion changes in whole placental tissue research, we developed a robust cell type-specific DNAm reference to estimate cell composition. We collated newly collected and existing cell type DNAm profiles quantified via Illumina EPIC or 450k microarrays. To estimate cell composition, we deconvoluted whole placental samples (n=36) with robust partial correlation based on the top 50 hyper- and hypomethylated sites per cell type. To test deconvolution performance, we evaluated RMSE in predicting principal component one of DNAm variation in 204 external placental samples. We analyzed DNAm profiles (n=368,435 sites) from 12 cell types: cytotrophoblasts (n=18), endothelial cells (n=19), Hofbauer cells (n=26), stromal cells (n=21), syncytiotrophoblasts (n=4), six lymphocyte types (n=36), and nucleated red blood cells (n=11). Median cell composition was consistent with placental biology: 60.4% syncytiotrophoblast, 17.1% stromal, 8.8% endothelial, 4.5% cytotrophoblast, 3.9% Hofbauer, 1.7% nucleated red blood cells, and 1.2% neutrophils. Our expanded reference outperformed an existing reference in predicting DNAm variation (15.4% variance explained, IQR=21.61) with cell composition estimates (RMSE:10.51 vs. 11.43, p-value<0.001). This cell type reference can robustly estimate cell composition from whole placental DNAm data to detect important cell types, reveal biological mechanisms, and improve casual inference.

scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison

Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or "pseudobulking" samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. Availability and Implementation: scDAPP is freely available for non-commercial usage as an R package under the MIT license. Source code, documentation and sample data are available at the GitHub (https://github.com/bioinfoDZ/scDAPP).

Compartmentalized CRISPR Reactions (CCR) for High-Throughput Screening of Guide RNA Potency and Specificity

CRISPR ribonucleoproteins (RNPs) use a variable segment in their guide RNA (gRNA) called a spacer to determine the DNA sequence at which the effector protein will exhibit nuclease activity and generate target-specific genetic mutations. However, nuclease activity with different gRNAs can vary considerably, in a spacer sequence-dependent manner that can be difficult to predict. While computational tools are helpful in predicting a CRISPR effector's activity and/or potential for off-target mutagenesis with different gRNAs, individual gRNAs must still be validated in vitro prior to their use. Here, we present compartmentalized CRISPR reactions (CCR) for screening large numbers of spacer/target/off-target combinations simultaneously in vitro for both CRISPR effector activity and specificity, by confining the complete CRISPR reaction of gRNA transcription, RNP formation, and CRISPR target cleavage within individual water-in-oil microemulsions. With CCR, large numbers of the candidate gRNAs (output by computational design tools) can be immediately validated in parallel, and we show that CCR can be used to screen hundreds of thousands of extended gRNA (x-gRNAs) variants that can completely block cleavage at off-target sequences while maintaining high levels of on-target activity. We expect CCR can help to streamline the gRNA generation and validation processes for applications in biological and biomedical research.

Development and Application of the CRISPR-dcas13d-eIF4G Translational Regulatory System to Inhibit Ferroptosis in Calcium Oxalate Crystal-Induced Kidney Injury

The CRISPR-Cas system, initially for DNA-level gene editing and transcription regulation, has expanded to RNA targeting with the Cas13d family, notably the RfxCas13d. This advancement allows for mRNA targeting with high specificity, particularly after catalytic inactivation, broadening the exploration of translation regulation. This study introduces a CRISPR-dCas13d-eIF4G fusion module, combining dCas13d with the eIF4G translation regulatory element, enhancing target mRNA translation levels. This module, using specially designed sgRNAs, selectively boosts protein translation in targeted tissue cells without altering transcription, leading to notable protein expression upregulation. This system is applied to a kidney stone disease model, focusing on ferroptosis-linked GPX4 gene regulation. By targeting GPX4 with sgRNAs, its protein expression is upregulated in human renal cells and mouse kidney tissue, countering ferroptosis and resisting calcium oxalate-induced cell damage, hence mitigating stone formation. This study evidences the CRISPR-dCas13d-eIF4G system's efficacy in eukaryotic cells, presenting a novel protein translation research approach and potential kidney stone disease treatment advancements.

Engineering plants using diverse CRISPR-associated proteins and deregulation of genome-edited crops

The CRISPR/Cas system comprises RNA-guided nucleases, the target specificity of which is directed by Watson-Crick base pairing of target loci with single guide (sg)RNA to induce the desired edits. CRISPR-associated proteins and other engineered nucleases are opening new avenues of research in crops to induce heritable mutations. Here, we review the diversity of CRISPR-associated proteins and strategies to deregulate genome-edited (GEd) crops by considering them to be close to natural processes. This technology ensures yield without penalties, advances plant breeding, and guarantees manipulation of the genome for desirable traits. DNA-free and off-target-free GEd crops with defined characteristics can help to achieve sustainable global food security under a changing climate, but need alignment of international regulations to operate in existing supply chains.

PIP4K2C inhibition reverses autophagic flux impairment induced by SARS-CoV-2

In search for broad-spectrum antivirals, we discovered a small molecule inhibitor, RMC-113, that potently suppresses the replication of multiple RNA viruses including SARS-CoV-2 in human lung organoids. We demonstrated selective dual inhibition of the lipid kinases PIP4K2C and PIKfyve by RMC-113 and target engagement by its clickable analog. Advanced lipidomics revealed alteration of SARS-CoV-2-induced phosphoinositide signature by RMC-113 and linked its antiviral effect with functional PIP4K2C and PIKfyve inhibition. We discovered PIP4K2C's roles in SARS-CoV-2 entry, RNA replication, and assembly/egress, validating it as a druggable antiviral target. Integrating proteomics, single-cell transcriptomics, and functional assays revealed that PIP4K2C binds SARS-CoV-2 nonstructural protein 6 and regulates virus-induced impairment of autophagic flux. Reversing this autophagic flux impairment is a mechanism of antiviral action of RMC-113. These findings reveal virus-induced autophagy regulation via PIP4K2C, an understudied kinase, and propose dual inhibition of PIP4K2C and PIKfyve as a candidate strategy to combat emerging viruses.

Principles, functions, and biological implications of m6A in plants

Over the past decade, N 6-methyladenosine (m6A) has emerged as a prevalent and dynamically regulated modification across the transcriptome; it has been reversibly installed, removed, and interpreted by specific binding proteins, and has played crucial roles in molecular and biological processes. Within this scope, we consolidate recent advancements of m6A research in plants regarding gene expression regulation, diverse physiologic and pathogenic processes, as well as crop trial implications, to guide discussions on challenges associated with and leveraging epitranscriptome editing for crop improvement.

SPLICER: A Highly Efficient Base Editing Toolbox That Enables In Vivo Therapeutic Exon Skipping

Exon skipping technologies enable exclusion of targeted exons from mature mRNA transcripts, which has broad applications in molecular biology, medicine, and biotechnology. Existing exon skipping techniques include antisense oligonucleotides, targetable nucleases, and base editors, which, while effective for specific applications at some target exons, remain hindered by shortcomings, including transient effects for oligonucleotides, genotoxicity for nucleases and inconsistent exon skipping for base editors. To overcome these limitations, we created SPLICER, a toolbox of next-generation base editors consisting of near-PAMless Cas9 nickase variants fused to adenosine or cytosine deaminases for the simultaneous editing of splice acceptor (SA) and splice donor (SD) sequences. Synchronized SA and SD editing with SPLICER improves exon skipping, reduces aberrant outcomes, including cryptic splicing and intron retention, and enables skipping of exons refractory to single splice-site editing. To demonstrate the therapeutic potential of SPLICER, we targeted APP exon 17, which encodes the amino acid residues that are cleaved to form the Aβ plaques in Alzheimer's disease. SPLICER reduced the formation of Aβ42 peptides in vitro and enabled efficient exon skipping in a mouse model of Alzheimer's disease. Overall, SPLICER is a widely applicable and efficient toolbox for exon skipping with broad therapeutic applications.

Pattern-Triggered Immunity and Effector-Triggered Immunity: crosstalk and cooperation of PRR and NLR-mediated plant defense pathways during host-pathogen interactions

The elucidation of the molecular basis underlying plant-pathogen interactions is imperative for the development of sustainable resistance strategies against pathogens. Plants employ a dual-layered immunological detection and response system wherein cell surface-localized Pattern Recognition Receptors (PRRs) and intracellular Nucleotide-Binding Leucine-Rich Repeat Receptors (NLRs) play pivotal roles in initiating downstream signalling cascades in response to pathogen-derived chemicals. Pattern-Triggered Immunity (PTI) is associated with PRRs and is activated by the recognition of conserved molecular structures, known as Pathogen-Associated Molecular Patterns. When PTI proves ineffective due to pathogenic effectors, Effector-Triggered Immunity (ETI) frequently confers resistance. In ETI, host plants utilize NLRs to detect pathogen effectors directly or indirectly, prompting a rapid and more robust defense response. Additionally epigenetic mechanisms are participating in plant immune memory. Recently developed technologies like CRISPR/Cas9 helps in exposing novel prospects in plant pathogen interactions. In this review we explore the fascinating crosstalk and cooperation between PRRs and NLRs. We discuss epigenomic processes and CRISPR/Cas9 regulating immune response in plants and recent findings that shed light on the coordination of these defense layers. Furthermore, we also have discussed the intricate interactions between the salicylic acid and jasmonic acid signalling pathways in plants, offering insights into potential synergistic interactions that would be harnessed for the development of novel and sustainable resistance strategies against diverse group of pathogens.