Reliable Genomic Integration Sites in Pseudomonas putida Identified by Two-Dimensional Transcriptome Analysis.

Genomic integration is commonly used to engineer stable production hosts. However, so far, for many microbial workhorses, only a few integration sites have been characterized, thereby restraining advanced strain engineering that requires multiple insertions. Here, we report on the identification of novel genomic integration sites, so-called landing pads, for Pseudomonas putida KT2440. We identified genomic regions with constant expression patterns under diverse experimental conditions by using RNA-Seq data. Homologous recombination constructs were designed to insert heterologous genes into intergenic sites in these regions, allowing condition-independent gene expression. Ten potential landing pads were characterized using four different msfGFP expression cassettes. An insulated probe sensor was used to study locus-dependent effects on recombinant gene expression, excluding genomic read-through of flanking promoters under changing cultivation conditions. While the reproducibility of expression in the landing pads was very high, the msfGFP signals varied strongly between the different landing pads, confirming a strong influence of the genomic context. To showcase that the identified landing pads are also suitable candidates for heterologous gene expression in other Pseudomonads, four equivalent landing pads were identified and characterized in Pseudomonas taiwanensis VLB120. This study shows that genomic "hot" and "cold" spots exist, causing strong promoter-independent variations in gene expression. This highlights that the genomic context is an additional parameter to consider when designing integrable genomic cassettes for tailored heterologous expression. The set of characterized genomic landing pads presented here further increases the genetic toolbox for deep metabolic engineering in Pseudomonads.

Directional integration and pathway enrichment analysis for multi-omics data.

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the datasets and penalises those with inconsistent directionality. To demonstrate our approach, we characterise gene and pathway regulation in IDH-mutant gliomas by jointly analysing transcriptomic, proteomic, and DNA methylation datasets. Directional integration of survival information in ovarian cancer reveals candidate biomarkers with consistent prognostic signals in transcript and protein expression. DPM is a general and adaptable framework for gene prioritisation and pathway analysis in multi-omics datasets.

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.

A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data.

Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information. We propose a novel VAE that utilizes priors from biological data to direct the network to learn a representation of the transcriptome that is based on understandable biological concepts. After benchmarking five different autoencoder architectures, we found that each succeeded in reducing the transcriptomes to 50 latent dimensions, which captured enough variation for accurate reconstruction. The simple, fully connected autoencoder, performs best across the benchmarks, but lacks the characteristic of having directly interpretable latent dimensions. The beta-weighted, prior-informed VAE implementation is able to solve the benchmarking tasks, and provide semantically accurate latent features equating to biological pathways. This study opens a new direction for differential pathway analysis in transcriptomics with increased transparency and interpretability.

Single-cell transcriptomics analysis of bullous pemphigoid unveils immune-stromal crosstalk in type 2 inflammatory disease.

Bullous pemphigoid (BP) is a type 2 inflammation- and immunity-driven skin disease, yet a comprehensive understanding of the immune landscape, particularly immune-stromal crosstalk in BP, remains elusive. Herein, using single-cell RNA sequencing (scRNA-seq) and in vitro functional analyzes, we pinpoint Th2 cells, dendritic cells (DCs), and fibroblasts as crucial cell populations. The IL13-IL13RA1 ligand-receptor pair is identified as the most significant mediator of immune-stromal crosstalk in BP. Notably, fibroblasts and DCs expressing IL13RA1 respond to IL13-secreting Th2 cells, thereby amplifying Th2 cell-mediated cascade responses, which occurs through the specific upregulation of PLA2G2A in fibroblasts and CCL17 in myeloid cells, creating a positive feedback loop integral to immune-stromal crosstalk. Furthermore, PLA2G2A and CCL17 contribute to an increased titer of pathogenic anti-BP180-NC16A autoantibodies in BP patients. Our work provides a comprehensive insight into BP pathogenesis and shows a mechanism governing immune-stromal interactions, providing potential avenues for future therapeutic research.

Single-cell total-RNA profiling unveils regulatory hubs of transcription factors.

Recent development of RNA velocity uses master equations to establish the kinetics of the life cycle of RNAs from unspliced RNA to spliced RNA (i.e., mature RNA) to degradation. To feed this kinetic analysis, simultaneous measurement of unspliced RNA and spliced RNA in single cells is greatly desired. However, the majority of single-cell RNA-seq chemistry primarily captures mature RNA species to measure gene expressions. Here, we develop a one-step total-RNA chemistry-based single-cell RNA-seq method: snapTotal-seq. We benchmark this method with multiple single-cell RNA-seq assays in their performance in kinetic analysis of cell cycle by RNA velocity. Next, with LASSO regression between transcription factors, we identify the critical regulatory hubs mediating the cell cycle dynamics. We also apply snapTotal-seq to profile the oncogene-induced senescence and identify the key regulatory hubs governing the entry of senescence. Furthermore, from the comparative analysis of unspliced RNA and spliced RNA, we identify a significant portion of genes whose expression changes occur in spliced RNA but not to the same degree in unspliced RNA, indicating these gene expression changes are mainly controlled by post-transcriptional regulation. Overall, we demonstrate that snapTotal-seq can provide enriched information about gene regulation, especially during the transition between cell states.

The downregulation of genes encoding muscle proteins have a potential role in the development of scrotal hernia in pigs.

BACKGROUND: Testicular descent is a physiological process regulated by many factors. Eventually, disturbances in the embryological/fetal development path facilitate the occurrence of scrotal hernia, a congenital malformation characterized by the presence of intestinal portions within the scrotal sac due to the abnormal expansion of the inguinal ring. In pigs, some genes have been related to this anomaly, but the genetic mechanisms involved remain unclear. This study aimed to investigate the expression profile of a set of genes potentially involved with the manifestation of scrotal hernia in the inguinal ring tissue. METHODS AND RESULTS: Tissue samples from the inguinal ring/canal of normal and scrotal hernia-affected male pigs with approximately 30 days of age were used. Relative expression analysis was performed using qPCR to confirm the expression profile of 17 candidate genes previously identified in an RNA-Seq study. Among them, the Myosin heavy chain 1 (MYH1), Desmin (DES), and Troponin 1 (TNNI1) genes were differentially expressed between groups and had reduced levels of expression in the affected animals. These genes encode proteins involved in the formation of muscle tissue, which seems to be important for increasing the resistance of the inguinal ring to the abdominal pressure, which is essential to avoid the occurrence of scrotal hernia. CONCLUSIONS: The downregulation of muscular candidate genes in the inguinal tissue clarifies the genetic mechanisms involved with this anomaly in its primary site, providing useful information for developing strategies to control this malformation in pigs and other mammals.

Combining Partial True Discovery Guarantee Procedures.

Closed testing has recently been shown to be optimal for simultaneous true discovery proportion control. It is, however, challenging to construct true discovery guarantee procedures in such a way that it focuses power on some feature sets chosen by users based on their specific interest or expertise. We propose a procedure that allows users to target power on prespecified feature sets, that is, "focus sets." Still, the method also allows inference for feature sets chosen post hoc, that is, "nonfocus sets," for which we deduce a true discovery lower confidence bound by interpolation. Our procedure is built from partial true discovery guarantee procedures combined with Holm's procedure and is a conservative shortcut to the closed testing procedure. A simulation study confirms that the statistical power of our method is relatively high for focus sets, at the cost of power for nonfocus sets, as desired. In addition, we investigate its power property for sets with specific structures, for example, trees and directed acyclic graphs. We also compare our method with AdaFilter in the context of replicability analysis. The application of our method is illustrated with a gene ontology analysis in gene expression data.

Single Cell Transcriptome Analysis During Development in Dictyostelium.

Dictyostelium represents a stripped-down model for understanding how cells make decisions during development. The complete life cycle takes around a day and the fully differentiated structure is composed of only two major cell types. With this apparent reduction in "complexity," single cell transcriptomics has proven to be a valuable tool in defining the features of developmental transitions and cell fate separation events, even providing causal information on how mechanisms of gene expression can feed into cell decision-making. These scientific outputs have been strongly facilitated by the ease of non-disruptive single cell isolation-allowing access to more physiological measures of transcript levels. In addition, the limited number of cell states during development allows the use of more straightforward analysis tools for handling the ensuing large datasets, which provides enhanced confidence in inferences made from the data. In this chapter, we will outline the approaches we have used for handling Dictyostelium single cell transcriptomic data, illustrating how these approaches have contributed to our understanding of cell decision-making during development.

Single-cell analysis of Crohn's disease: Unveiling heterogeneity and evaluating ustekinumab outcomes.

BACKGROUND: The present study aimed to dissect the cellular complexity of Crohn's disease (CD) using single-cell RNA sequencing, focusing on identifying key cell populations and their transcriptional profiles in inflamed tissue. METHODS: We applied scRNA-sequencing to compare the cellular composition of CD patients with healthy controls, utilizing Seurat for clustering and annotation. Differential gene expression analysis and protein-protein interaction networks were constructed to identify crucial genes and pathways. RESULTS: Our study identified eight distinct cell types in CD, highlighting crucial fibroblast and T cell interactions. The analysis revealed key cellular communications and identified significant genes and pathways involved in the disease's pathology. The role of fibroblasts was underscored by elevated expression in diseased samples, offering insights into disease mechanisms and potential therapeutic targets, including responses to ustekinumab treatment, thus enriching our understanding of CD at a molecular level. CONCLUSIONS: Our findings highlight the complex cellular and molecular interplay in CD, suggesting new biomarkers and therapeutic targets, offering insights into disease mechanisms and treatment implications.

Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.

Transcriptome Dynamics and Cell Dialogs Between Oocytes and Granulosa Cells in Mouse Follicle Development.

The development and maturation of follicles is a sophisticated and multistage process. The dynamic gene expression of oocytes and their surrounding somatic cells and the dialogs between these cells are critical to this process. In this study, we accurately classified the oocyte and follicle development into nine stages and profiled the gene expression of mouse oocytes and their surrounding granulosa cells and cumulus cells. The clustering of the transcriptomes showed the trajectories of two distinct development courses of oocytes and their surrounding somatic cells. Gene expression changes precipitously increased at Type 4 stage and drastically dropped afterward within both oocytes and granulosa cells. Moreover, the number of differentially expressed genes between oocytes and granulosa cells dramatically increased at Type 4 stage, most of which persistently passed on to the later stages. Strikingly, cell communications within and between oocytes and granulosa cells became active from Type 4 stage onward. Cell dialogs connected oocytes and granulosa cells in both unidirectional and bidirectional manners. TGFB2/3, TGFBR2/3, INHBA/B, and ACVR1/1B/2B of TGF-ß signaling pathway functioned in the follicle development. NOTCH signaling pathway regulated the development of granulosa cells. Additionally, many maternally DNA methylation- or H3K27me3-imprinted genes remained active in granulosa cells but silent in oocytes during oogenesis. Collectively, Type 4 stage is the key turning point when significant transcription changes diverge the fate of oocytes and granulosa cells, and the cell dialogs become active to assure follicle development. These findings shed new insights on the transcriptome dynamics and cell dialogs facilitating the development and maturation of oocytes and follicles.

Circulating tumor extracellular vesicles to monitor metastatic prostate cancer genomics and transcriptomic evolution.

Extracellular vesicles (EVs) secreted by tumors are abundant in plasma, but their potential for interrogating the molecular features of tumors through multi-omic profiling remains widely unexplored. Genomic and transcriptomic profiling of circulating EV-DNA and EV-RNA isolated from in vitro and in vivo models of metastatic prostate cancer (mPC) reveal a high contribution of tumor material to EV-loaded DNA/RNA, validating the findings in two cohorts of longitudinal plasma samples collected from patients during androgen receptor signaling inhibitor (ARSI) or taxane-based therapy. EV-DNA genomic features recapitulate matched-patient biopsies and circulating tumor DNA (ctDNA) and associate with clinical progression. We develop a novel approach to enable transcriptomic profiling of EV-RNA (RExCuE). We report how the transcriptome of circulating EVs is enriched for tumor-associated transcripts, captures certain patient and tumor features, and reflects on-therapy tumor adaptation changes. Altogether, we show that EV profiling enables longitudinal transcriptomic and genomic profiling of mPC in liquid biopsy.

Transcriptomic Analysis During the Induction of Somatic Embryogenesis in Coffea canephora.

Omic tools have changed the way of doing research in experimental biology. The somatic embryogenesis (SE) study has not been immune to this benefit. The transcriptomic tools have been used to compare the genes expressed during the induction of SE with the genes expressed in zygotic embryogenesis or to compare the development of the different stages embryos go through. It has also been used to compare the expression of genes during the development of calli from which SE is induced, as well as many other applications. The protocol described here is employed in our laboratory to extract RNA and generate several transcriptomes for the study of SE on Coffea canephora.

S1P Signaling Genes as Prominent Drivers of BCR-ABL1-Independent Imatinib Resistance and Six Herbal Compounds as Potential Drugs for Chronic Myeloid Leukemia.

Imatinib (IM), a breakthrough in chronic myeloid leukemia (CML) treatment, is accompanied by discontinuation challenges owing to drug intolerance. Although BCR-ABL1 mutation is a key cause of CML resistance, understanding mechanisms independent of BCR-ABL1 is also important. This study investigated the sphingosine-1-phosphate (S1P) signaling-associated genes (SphK1 and S1PRs) and their role in BCR-ABL1-independent resistant CML, an area currently lacking investigation. Through comprehensive transcriptomic analysis of IM-sensitive and IM-resistant CML groups, we identified the differentially expressed genes and found a notable upregulation of SphK1, S1PR2, and S1PR5 in IM-resistant CML. Functional annotation revealed their roles in critical cellular processes such as proliferation and GPCR activity. Their network analysis uncovered significant clusters, emphasizing the interconnectedness of the S1P signaling genes. Further, we identified interactors such as BIRC3, TRAF6, and SRC genes, with potential implications for IM resistance. Additionally, receiver operator characteristic curve analysis suggested these genes' potential as biomarkers for predicting IM resistance. Network pharmacology analysis identified six herbal compounds-ampelopsin, ellagic acid, colchicine, epigallocatechin-3-gallate, cucurbitacin B, and evodin-as potential drug candidates targeting the S1P signaling genes. In summary, this study contributes to efforts to better understand the molecular mechanisms underlying BCR-ABL1-independent CML resistance. Moreover, the S1P signaling genes are promising therapeutic targets and plausible new innovation avenues to combat IM resistance in cancer clinical care in the future.

GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

Pan-cancer transcriptome analysis reveals widespread regulation through alternative tandem transcription initiation.

Abnormal transcription initiation from alternative first exon has been reported to promote tumorigenesis. However, the prevalence and impact of gene expression regulation mediated by alternative tandem transcription initiation were mostly unknown in cancer. Here, we developed a robust computational method to analyze alternative tandem transcription start site (TSS) usage from standard RNA sequencing data. Applying this method to pan-cancer RNA sequencing datasets, we observed widespread dysregulation of tandem TSS usage in tumors, many of which were independent of changes in overall expression level or alternative first exon usage. We showed that the dynamics of tandem TSS usage was associated with epigenomic modulation. We found that significant 5' untranslated region shortening of gene TIMM13 contributed to increased protein production, and up-regulation of TIMM13 by CRISPR-mediated transcriptional activation promoted proliferation and migration of lung cancer cells. Our findings suggest that dysregulated tandem TSS usage represents an addtional layer of cancer-associated transcriptome alterations.

InstaPrism: an R package for fast implementation of BayesPrism.

SUMMARY: Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently been proposed and has subsequently been shown to be superior in accuracy and robustness against model misspecifications by independent studies; however, given that BayesPrism relies on Gibbs sampling, it is orders of magnitude more computationally expensive than standard approaches. Here, we introduce the InstaPrism package which re-implements BayesPrism in a derandomized framework by replacing the time-consuming Gibbs sampling step with a fixed-point algorithm. We demonstrate that the new algorithm is effectively equivalent to BayesPrism while providing a considerable speed and memory advantage. Furthermore, the InstaPrism package is equipped with a precompiled, curated set of references tailored for a variety of cancer types, streamlining the deconvolution process. AVAILABILITY AND IMPLEMENTATION: The package InstaPrism is freely available at: https://github.com/humengying0907/InstaPrism. The source code and evaluation pipeline used in this paper can be found at: https://github.com/humengying0907/InstaPrismSourceCode.

Parallel single B cell transcriptomics to elucidate pig B cell repertoire.

Pork is the most widely consumed meat on the planet, placing swine health as a critical factor for both the world economy and the food industry. Infectious diseases in pigs not only threaten these sectors but also raise zoonotic concerns, as pigs can act as "mixing vessels" for several animals and human viruses and can lead to the emergence of new viruses that are capable of infecting humans. Several efforts are ongoing to develop pig vaccines, albeit with limited success. This has been largely attributed to the complex nature of pig infections and incomplete understanding of the pig immune responses. Additionally, pig has been suggested to be a good experimental model to study viral infections (e.g., human influenza). Despite the significant importance of studying pig immunology for developing infection models, zoonosis, and the crucial need to develop better swine vaccines, there is still very limited information on the response of the swine adaptive immune system to several emerging pathogens. Particularly, very little is known about the pig B cell repertoire upon infection. Understanding the B cell repertoire is especially crucial towards designing better vaccines, predicting zoonosis and can provide insights into developing new diagnostic agents. Here, we developed methods for performing parallel single pig B cell (up to 10,000 B cells) global and immunoglobulin transcriptome sequencing. We then adapted a computational pipeline previously built for human/mouse sequences, to now analyze pig sequences. This allowed us to comprehensively map the B cell repertoire and get paired antibody sequences from pigs in a single parallel sequencing experiment. We believe that these approaches will have significant implications for swine diseases, particularly in the context of swine mediated zoonosis and swine and human vaccine development.