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Summary Spatial transcriptomics (ST) technologies enable the measurement of mRNA expression while simultaneously capturing spot locations. By integrating ST data, the 3D structure of a tissue can be reconstructed, yielding a comprehensive understanding of the tissue's intricacies. Nevertheless, a computational challenge persists: how to remove batch effects while preserving genuine biological structure variations across ST data. To address this, we introduce Graspot, a gr aph a ttention network designed for sp atial transcriptomics data integration with unbalanced o ptimal t ransport. Graspot adeptly harnesses both gene expression and spatial information to align common structures across multiple ST datasets. It embeds multiple ST datasets into a unified latent space, facilitating the partial alignment of spots from different slices. Demonstrating superior performance compared to existing methods on four real ST datasets, Graspot excels in ST data integration, including tasks that require partial alignment. In particular, Graspot efficiently integrates multiple ST slices and guides coordinate alignment. In addition, Graspot accurately aligns the spatio-temporal transcriptomics data to reconstruct human heart developmental processes. Availability and implementation Graspot software is available at https://github.com/zhan009/Graspot. [ABSTRACT FROM AUTHOR]

Summary Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE , an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE 's predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path. Availability and implementation The data and code are publicly available at https://github.com/rsinghlab/scNODE. [ABSTRACT FROM AUTHOR]

Motivation Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity. Results Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest. Availability and implementation https://github.com/song0309/asGNN/ [ABSTRACT FROM AUTHOR]

Transposable elements make up substantial proportions of eukaryotic genomes and many are thought to be remnants of ancient viral infections. Current research has begun to highlight the role transposable elements can play in the immune system response to infections. However, most of our knowledge about transposable element expression during infection is limited by the specific host and pathogen factors from each study, making it difficult to compare studies and develop broader patterns regarding the role of transposable elements during infection. Here, we use the tools and resources available in the model, Drosophila melanogaster , to analyze multiple gene expression datasets of flies subject to bacterial, fungal, and viral infections. We analyzed differences in pathogen species, host genotype, host tissue, and sex to understand how these factors impact transposable element expression during infection. Our results highlight both shared and unique transposable element expression patterns between pathogens and suggest a larger effect of pathogen factors over host factors for influencing transposable element expression. [ABSTRACT FROM AUTHOR]

Mycobacterium phage Adephagia is a cluster K phage that infects Mycobacterium smegmatis and some strains of Mycobacterium pathogens. Adephagia has a siphoviral virion morphology and is temperate. Its genome is 59,646 bp long and codes for one tRNA gene and 94 predicted protein-coding genes; most genes not associated with virion structure and assembly are functionally ill-defined. Here, we determined the Adephagia gene expression patterns in lytic and lysogenic growth and used structural predictions to assign additional gene functions. We characterized 66 nonstructural genes for their toxic phenotypes when expressed in M. smegmatis , and we show that 25 of these (38%) are either toxic or strongly inhibit growth, resulting in either reduced viability or small colony sizes. Some of these genes are predicted to be involved in DNA metabolism or regulation, but others are of unknown function. We also characterize the HicAB-like toxin–antitoxin (TA) system encoded by Adephagia (gp91 and gp90, respectively) and show that the gp90 antitoxin is lysogenically expressed, abrogates gp91 toxicity, and is required for normal lytic and lysogenic growth. [ABSTRACT FROM AUTHOR]

Zinc (Zn) is a major soil contaminant and high Zn levels can disrupt growth, survival, and reproduction of fungi. Some fungal species evolved Zn tolerance through cell processes mitigating Zn toxicity, although the genes and detailed mechanisms underlying mycorrhizal fungal Zn tolerance remain unexplored. To fill this gap in knowledge, we investigated the gene expression of Zn tolerance in the ectomycorrhizal fungus Suillus luteus. We found that Zn tolerance in this species is mainly a constitutive trait that can also be environmentally dependent. Zinc tolerance in S. luteus is associated with differences in the expression of genes involved in metal exclusion and immobilization, as well as recognition and mitigation of metal-induced oxidative stress. Differentially expressed genes were predicted to be involved in transmembrane transport, metal chelation, oxidoreductase activity, and signal transduction. Some of these genes were previously reported as candidates for S. luteus Zn tolerance, while others are reported here for the first time. Our results contribute to understanding the mechanisms of fungal metal tolerance and pave the way for further research on the role of fungal metal tolerance in mycorrhizal associations. [ABSTRACT FROM AUTHOR]

Intermediate wheatgrass (IWG) is a perennial grass that produces nutritious grain while offering substantial ecosystem services. Commercial varieties of this crop are mostly synthetic panmictic populations that are developed by intermating a few selected individuals. As development and generation advancement of these synthetic populations is a multiyear process, earlier synthetic generations are tested by the breeders and subsequent generations are released to the growers. A comparison of generations within IWG synthetic cultivars is currently lacking. In this study, we used simulation models and genomic prediction to analyze population differences and trends of genetic variance in 4 synthetic generations of MN-Clearwater, a commercial cultivar released by the University of Minnesota. Little to no differences were observed among the 4 generations for population genetic, genetic kinship, and genome-wide marker relationships measured via linkage disequilibrium. A reduction in genetic variance was observed when 7 parents were used to generate synthetic populations while using 20 led to the best possible outcome in determining population variance. Genomic prediction of plant height, free threshing ability, seed mass, and grain yield among the 4 synthetic generations showed a few significant differences among the generations, yet the differences in values were negligible. Based on these observations, we make 2 major conclusions: (1) the earlier and latter synthetic generations of IWG are mostly similar to each other with minimal differences and (2) using 20 genotypes to create synthetic populations is recommended to sustain ample genetic variance and trait expression among all synthetic generations. [ABSTRACT FROM AUTHOR]

Strigolactones (SLs), carotenoid-derived plant hormones, govern the growth and development of both monocotyledonous and dicotyledonous plants. DWARF27 (D27), a plastid-targeted protein located at the initiation site of the core pathway in SL synthesis, plays a crucial role in regulating plant tillering (branching). In rice (Oryza sativa) and wheat (Triticum aestivum), OsD27 and TaD27-B proteins modulate the number of plant tillers by participating in SL biosynthesis. Similarly, AtD27 in Arabidopsis thaliana is required for SL production and has a significant impact on phenotypic changes related to branching. At the same time, TaD27 in wheat has been confirmed as a functional orthologue of AtD27 in Arabidopsis , and both Psathyrostachys juncea and wheat belong to the Triticeae , so we speculate that PjD27 gene may also have the same function as AtD27 in Arabidopsis. In this study, we initially screened the PjD27 gene significantly associated with tillering regulation through transcriptome data analysis and subsequently validated its expression levels using qRT-PCR analysis. Furthermore, we conducted phylogenetic analysis using amino acid sequences from 41 species, including P. juncea , to identify closely related species of P. juncea. Here, we analyze the conservation of D27 protein among P. juncea , rice, wheat, and Arabidopsis and provide preliminary evidence suggesting that PjD27 protein is an orthologue of D27 protein in Arabidopsis. Through reverse genetics, we demonstrate the crucial role of PjD27 in regulating plant branching, establishing it as a functional orthologue of D27 in Arabidopsis. Furthermore, following transient expression in tobacco (Nicotiana tabacum), we demonstrate that the subcellular location of the PjD27 protein is consistent with the cellular location of TaD27-B in wheat. Quantitative analysis of SLs shows that PjD27 is a key gene regulating tillering (branching) by participating in SL biosynthesis. By elucidating the function of the PjD27 gene, our findings provide valuable genetic resources for new germplasm creation and improving grain yield in P. juncea. [ABSTRACT FROM AUTHOR]

Gene regulatory networks specify the gene expression patterns needed for traits to develop. Differences in these networks can result in phenotypic differences between organisms. Although loss-of-function genetic screens can identify genes necessary for trait formation, gain-of-function screens can overcome genetic redundancy and identify loci whose expression is sufficient to alter trait formation. Here, we leveraged transgenic lines from the Transgenic RNAi Project at Harvard Medical School to perform both gain- and loss-of-function CRISPR/Cas9 screens for abdominal pigmentation phenotypes. We identified measurable effects on pigmentation patterns in the Drosophila melanogaster abdomen for 21 of 55 transcription factors in gain-of-function experiments and 7 of 16 tested by loss-of-function experiments. These included well-characterized pigmentation genes, such as bab1 and dsx , and transcription factors that had no known role in pigmentation, such as slp2. Finally, this screen was partially conducted by undergraduate students in a Genetics Laboratory course during the spring semesters of 2021 and 2022. We found this screen to be a successful model for student engagement in research in an undergraduate laboratory course that can be readily adapted to evaluate the effect of hundreds of genes on many different Drosophila traits, with minimal resources. [ABSTRACT FROM AUTHOR]

Mutating replication-dependent (RD) histone genes is an important tool for understanding chromatin-based epigenetic regulation. Deploying this tool in metazoans is particularly challenging because RD histones in these organisms are typically encoded by many genes, often located at multiple loci. Such gene arrangements make the ability to generate homogenous histone mutant genotypes by site-specific gene editing quite difficult. Drosophila melanogaster provides a solution to this problem because the RD histone genes are organized into a single large tandem array that can be deleted and replaced with transgenes containing mutant histone genes. In the last ∼15 years several different RD histone gene replacement platforms were developed using this simple strategy. However, each platform contains weaknesses that preclude full use of the powerful developmental genetic capabilities available to Drosophila researchers. Here we describe the development of a newly engineered platform that rectifies many of these weaknesses. We used CRISPR to precisely delete the RD histone gene array (HisC), replacing it with a multifunctional cassette that permits site-specific insertion of either one or two synthetic gene arrays using selectable markers. We designed this cassette with the ability to selectively delete each of the integrated gene arrays in specific tissues using site-specific recombinases. We also present a method for rapidly synthesizing histone gene arrays of any genotype using Golden Gate cloning technologies. These improvements facilitate the generation of histone mutant cells in various tissues at different stages of Drosophila development and provide an opportunity to apply forward genetic strategies to interrogate chromatin structure and gene regulation. [ABSTRACT FROM AUTHOR]

Centromeric localization of evolutionarily conserved CENP-A (Cse4 in Saccharomyces cerevisiae) is essential for chromosomal stability. Mislocalization of overexpressed CENP-A to noncentromeric regions contributes to chromosomal instability in yeasts, flies, and humans. Overexpression and mislocalization of CENP-A observed in many cancers are associated with poor prognosis. Previous studies have shown that F-box proteins, Cdc4 and Met30 of the Skp, Cullin, F-box ubiquitin ligase cooperatively regulate proteolysis of Cse4 to prevent Cse4 mislocalization and chromosomal instability under normal physiological conditions. Mck1 -mediated phosphorylation of Skp, Cullin, F-box- Cdc4 substrates such as Cdc6 and Rcn1 enhances the interaction of the substrates with Cdc4. Here, we report that Mck1 interacts with Cse4 , and Mck1 -mediated proteolysis of Cse4 prevents Cse4 mislocalization for chromosomal stability. Our results showed that mck1 Δ strain overexpressing CSE4 (GAL- CSE4) exhibits lethality, defects in ubiquitin-mediated proteolysis of Cse4 , mislocalization of Cse4 , and reduced Cse4 – Cdc4 interaction. Strain expressing GAL- cse4 -3A with mutations in three potential Mck1 phosphorylation consensus sites (S10, S16, and T166) also exhibits growth defects, increased stability with mislocalization of Cse4 -3A, chromosomal instability, and reduced interaction with Cdc4. Constitutive expression of histone H3 (Δ16H3) suppresses the chromosomal instability phenotype of GAL- cse4 -3A strain, suggesting that the chromosomal instability phenotype is linked to Cse4 -3A mislocalization. We conclude that Mck1 and its three potential phosphorylation sites on Cse4 promote Cse4 – Cdc4 interaction and this contributes to ubiquitin-mediated proteolysis of Cse4 preventing its mislocalization and chromosomal instability. These studies advance our understanding of pathways that regulate cellular levels of CENP-A to prevent mislocalization of CENP-A in human cancers. [ABSTRACT FROM AUTHOR]

Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analyses using TOPMed whole-genome sequencing-derived genotype data and RNA-sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate of 5%, we identified 17,630 unique eQTL credible sets covering 16,538 unique genes; and 24,525 unique sQTL credible sets covering 9,605 unique genes, with lead QTL at P < 5e−8. About 24% of independent eQTLs and independent sQTLs with a minor allele frequency > 1% in JHS were rare (minor allele frequency < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results. [ABSTRACT FROM AUTHOR]

The faster-Z/X hypothesis predicts that sex-linked genes should diverge faster than autosomal genes. However, studies across different lineages have shown mixed support for this effect. So far, most analyses have focused on old and well-differentiated sex chromosomes, but less is known about the divergence of more recently acquired neo-sex chromosomes. In Lepidoptera (moths and butterflies), Z-autosome fusions are frequent, but the evolutionary dynamics of neo-Z chromosomes have not been explored in detail. Here, we analyzed the faster-Z effect in Leptidea sinapis , a butterfly with three Z chromosomes. We show that the neo-Z chromosomes have been acquired stepwise, resulting in strata of differentiation and masculinization. While all Z chromosomes showed evidence of the faster-Z effect, selection for genes on the youngest neo-Z chromosome (Z3) appears to have been hampered by a largely intact, homologous neo-W chromosome. However, the intermediately aged neo-Z chromosome (Z2), which lacks W gametologs, showed fewer evolutionary constraints, resulting in particularly fast evolution. Our results therefore support that neo-sex chromosomes can constitute temporary hot-spots of adaptation and divergence. The underlying dynamics are likely causally linked to shifts in selective constraints, evolution of gene expression, and degeneration of W-linked gametologs which gradually expose Z-linked genes to selection. [ABSTRACT FROM AUTHOR]

Introduction High-grade serous ovarian cancer (HGSOC) is characterized by high mortality and prevalent recurrences. This study investigates the prognostic value of phosphoglycerate dehydrogenase (PHGDH) in HGSOC which has been linked to metabolic reprogramming and recurrences in other cancers. Methods Data from 306 patients with advanced-stage HGSOC treated between 2008 and 2015 were analyzed. PHGDH expression levels were determined using immunohistochemistry and categorized as "low" or "high." Results PHGDH-high was associated with higher FIGO stage and increased use of neoadjuvant chemotherapy. Patients with PHGDH-high tumors had significantly worse survival than PHDH-low, even after adjusting for confounding factors. [ABSTRACT FROM AUTHOR]

Background No consensus has been reached regarding the optimal chemotherapy for metastatic extramammary Paget's disease (EMPD), a rare cutaneous adenocarcinoma, because of the lack of solid evidence from prospective trials. However, the immunohistochemical profile of EMPD reportedly resembles that of breast cancer, particularly in terms of human epidermal growth factor receptor 2 (HER2) expression, suggesting that HER2 is a promising therapeutic target for advanced HER2-positive EMPD. Methods In this phase II single-arm trial, 13 Japanese patients received intravenous trastuzumab (loading dose of 8 mg/kg and maintenance dose of 6 mg/kg) and docetaxel (75 mg/m2) every 3 weeks for up to 2 years. The docetaxel dose was reduced or discontinued according to its toxicity. The primary trial endpoints were objective response rate (ORR) after 3 cycles of treatment and safety throughout the study period. Results All 13 patients completed 3 cycles of combination therapy. The median follow-up was 27.9 months. The ORR was 76.9% (n = 10/13; 90% CI, 50.5-93.4). Frequently observed adverse events were neutropenia (100%), hypoalbuminemia (84.6%), and mucocutaneous infection (84.6%), all of which were well tolerated. Conclusion The combination of docetaxel and trastuzumab demonstrated a favorable clinical effect and acceptable tolerability, which makes it a good treatment option for HER2-positive metastatic EMPD (ClinicalTrials.gov Identifier: UMIN000021311, jRCTs031180073). [ABSTRACT FROM AUTHOR]

Objective Patients with radioiodine-refractory (RAIR) differentiated thyroid carcinoma (DTC; RAIR-DTC) have a poor prognosis. The aim of this study was to provide new insights and possibilities for the diagnosis and treatment of RAIR-DTC. Methods The metabolomics of 24 RAIR-DTC and 18 non-radioiodine-refractory (NonRAIR) DTC patients samples were analyzed by liquid chromatograph-mass spectrometry. Cellular radioiodine uptake was detected with γ counter. Sodium iodide symporter (NIS) expression and thyroid stimulating hormone receptor (TSHR) were measured by Western blot analysis. CCK8 and colony formation assays were used to measure cellular proliferation. Scratch and transwell assays were performed to assess cell migration and invasion. Annexin V/PI staining was used to detect cell apoptosis. Cell growth in vivo was evaluated by a tumor xenograft model. The acetoacetate (AcAc) level was measured by ELISA. Pathological changes, Ki67, NIS, and TSHR expression were investigated by immunohistochemistry. Results The metabolite profiles of RAIR could be distinguished from those of NonRAIR, with AcAc significantly lower in RAIR. The significantly different metabolic pathway was ketone body metabolism. AcAc increased NIS and TSHR expression and improved radioiodine uptake. AcAc inhibited cell proliferation, migration, and invasion, and as well promoted cell apoptosis. Ketogenic diet (KD) elevated AcAc levels and significantly suppressed tumor growth, as well as improved NIS and TSHR expression. Conclusion Significant metabolic differences were observed between RAIR and NonRAIR, and ketone body metabolism might play an important role in RAIR-DTC. AcAc improved cellular iodine uptake and had antitumor effects for thyroid carcinoma. KD might be a new therapeutic strategy for RAIR-DTC. [ABSTRACT FROM AUTHOR]

Aims Examining the systemic biological processes in the heterogeneous syndrome of heart failure with reduced ejection fraction (HFrEF), as reflected by circulating proteins, in relation to echocardiographic characteristics, may provide insights into heart failure pathophysiology. We investigated the link of 4210 repeatedly measured circulating proteins with repeatedly measured echocardiographic parameters as well as with elevated left atrial pressure (LAP), in patients with HFrEF, to provide insights into underlying mechanisms. Methods and results In 173 patients with HFrEF, we performed 6-monthly echocardiography and trimonthly blood sampling during a median follow-up of 2.7 (inter-quartile range: 2.5–2.8) years. We investigated circulating proteins in relation to echocardiographic parameters of left ventricular [left ventricular ejection fraction (LVEF), global longitudinal strain (GLS)] and left atrial function [left atrial reservoir strain (LASr)] and elevated LAP (E / e ʹ ratio >15) and used gene enrichment analyses to identify underlying pathophysiological processes. We found 723, 249, 792, and 427 repeatedly measured proteins, with significant associations with LVEF, GLS, LASr, and E / e ʹ ratio, respectively. Proteins associated with LASr reflected pathophysiological mechanisms mostly related to the extracellular matrix. Proteins associated with GLS reflected cardiovascular biological processes and diseases, whereas those associated with LVEF reflected processes involved in the sympathetic nervous system. Moreover, 49 proteins were associated with elevated LAP; after correction for LVEF, three proteins remained: cystatin-D, fibulin-5, and HSP40. Conclusion Circulating proteins show varying associations with different echocardiographic parameters in patients with HFrEF. These findings suggest that pathways involved in atrial and ventricular dysfunction, as reflected by the plasma proteome, are distinct. [ABSTRACT FROM AUTHOR]

Background Chronic immune activation is one of the hallmarks of human immunodeficiency virus (HIV) pathogenesis. Persistent upregulation of interferons (IFNs) and interferon-stimulated genes (ISGs) has previously been associated with chronic immune activation and HIV progression. Here a longitudinal analysis of the IFN and ISG response during HIV infection was performed to gain insights into the ongoing immune activation during HIV infection. Methods IFN and ISG levels were determined using quantitative polymerase chain reaction in peripheral blood mononuclear cells of people with HIV at pre-seroconversion, during acute and chronic HIV infection, and during suppressive antiretroviral therapy (ART). Results HIV infection induced the expression of a set of 4 ISGs—RSAD2, ISG15, IFI44L, and IFI27—which remained upregulated during chronic infection. This set of ISGs showed no clear correlations with T-cell activation as determined by co-expression of CD38 and HLA-DR. However, a strong correlation with monocyte activation marker soluble CD163 in serum was found. Furthermore, the expression of this ISG cluster was predictive of viral load before ART initiation and, on ART, expression levels normalized to pre-seroconversion levels. Conclusions The results presented here suggests that ISG expression is linked to monocyte activation, possibly driven by viral replication. [ABSTRACT FROM AUTHOR]

High fish predation pressure can trigger "induced defense" in Daphnia species, resulting in phenotypic plasticity in morphology, behavior, or life-history traits. The molecular mechanisms of defense morphogenesis (e.g. the tail spine and helmet) in Daphnia remain unclear. In the present study, the tail spine, helmet, and body of Daphnia galeata under fish and non-fish kairomones conditions were collected for transcriptome analysis. A total of 24 candidate genes related to the morphological defense of D. galeata were identified, including 2 trypsin, one cuticle protein, 1 C1qDC protein, and 2 ferritin genes. The function of the Dagcut gene (D. galeata cuticle protein gene) in relation to tail spine morphology was assessed using RNA interference (RNAi). Compared with the EGFP (Enhanced green fluorescent protein) treatment, after RNAi, the expression levels of the Dagcut gene (D. galeata cuticle protein gene) showed a significant decrease. Correspondingly, the tail spines of the offspring produced by D. galeata after RNAi of the Dagcut gene appeared curved during the experiment. In whole-mount in situ hybridization, a clear signal site was detected on the tail spine of D. galeata before RNAi which disappeared after RNAi. Our results suggest that the Dagcut gene may play an important role in tail spine formation of D. galeata , and will provide a theoretical basis for studying the molecular mechanisms of the morphological plasticity in cladocera in the future. [ABSTRACT FROM AUTHOR]

Motivation High-throughput technologies yield a broad spectrum of multi-omics datasets, which offer unparalleled insights into complex biological systems. However, effectively analyzing this diverse array of data presents challenges, considering factors such as species diversity, data types, costs, and limitations of the available tools. Results Herein, we present ExpOmics, a comprehensive web platform featuring 7 applications and 4 toolkits, with 28 customizable analysis functions spanning various analyses of differential expression, co-expression, Weighted Gene Co-expression Network Analysis (WGCNA), feature selection, and functional enrichment. ExpOmics allows users to upload and explore multi-omics data without organism restrictions, supporting various expression data, including genes, mRNAs, lncRNAs, miRNAs, circRNAs, piRNAs, and proteins and is compatible with diverse gene nomenclatures and expression values. Moreover, ExpOmics enables users to analyze 22 427 transcriptomic datasets of 196 cancer subtypes sourced from 63 projects of The Cancer Genome Atlas Program (TCGA) to identify cancer biomarkers. The analysis results from ExpOmics are presented in high-quality graphical formats suitable for publication and are available for free download. A case study using ExpOmics identified two potential oncogenes, SERPINE1 and SLC43A1 , that may regulate colorectal cancer through distinct biological processes. In summary, ExpOmics can serves as a robust platform for global researchers to explore multi-omics data, gain biological insights, and formulate testable hypotheses. Availability and implementation ExpOmics is available at http://www.biomedical-web.com/expomics. [ABSTRACT FROM AUTHOR]

Motivation The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular processes. To advance our comprehension of gene regulatory networks, it is essential to develop computational tools for accurately identifying the subcellular localizations of mRNAs. However, considering multi-localization phenomena remains limited in existing approaches, with none considering the influence of RNA's secondary structure. Results In this study, we propose Allocator, a multi-view parallel deep learning framework that seamlessly integrates the RNA sequence-level and structure-level information, enhancing the prediction of mRNA multi-localization. The Allocator models equip four efficient feature extractors, each designed to handle different inputs. Two are tailored for sequence-based inputs, incorporating multilayer perceptron and multi-head self-attention mechanisms. The other two are specialized in processing structure-based inputs, employing graph neural networks. Benchmarking results underscore Allocator's superiority over state-of-the-art methods, showcasing its strength in revealing intricate localization associations. Availability and implementation The webserver of Allocator is available at http://Allocator.unimelb-biotools.cloud.edu.au ; the source code and datasets are available on GitHub (https://github.com/lifuyi774/Allocator) and Zenodo (https://doi.org/10.5281/zenodo.13235798). [ABSTRACT FROM AUTHOR]

Motivation Generalized linear mixed models (GLMMs), such as the negative-binomial or Poisson linear mixed model, are widely applied to single-cell RNA sequencing data to compare transcript expression between different conditions determined at the subject level. However, the model is computationally intensive, and its relative statistical performance to pseudobulk approaches is poorly understood. Results We propose offset-pseudobulk as a lightweight alternative to GLMMs. We prove that a count-based pseudobulk equipped with a proper offset variable has the same statistical properties as GLMMs in terms of both point estimates and standard errors. We confirm our findings using simulations based on real data. Offset-pseudobulk is substantially faster (>×10) and numerically more stable than GLMMs. Availability and implementation Offset pseudobulk can be easily implemented in any generalized linear model software by tweaking a few options. The codes can be found at https://github.com/hanbin973/pseudobulk_is_mm. [ABSTRACT FROM AUTHOR]

Motivation Although human tissues carry out common molecular processes, gene expression patterns can distinguish different tissues. Traditional informatics methods, primarily at the gene level, overlook the complexity of alternative transcript variants and protein isoforms produced by most genes, changes in which are linked to disease prognosis and drug resistance. Results We developed TransTEx (Tran s cript-level T issue E xpression), a novel tissue-specificity scoring method, for grouping transcripts into four expression groups. TransTEx applies sequential cut-offs to tissue-wise transcript probability estimates, subsampling-based P -values and fold-change estimates. Application of TransTEx on GTEx mRNA-seq data divided 199 166 human transcripts into different groups as 17 999 tissue-specific (TSp), 7436 tissue-enhanced, 36 783 widely expressed (Wide), 79 191 lowly expressed (Low), and 57 757 no expression (Null) transcripts. Testis has the most (13 466) TSp isoforms followed by liver (890), brain (701), pituitary (435), and muscle (420). We found that the tissue specificity of alternative transcripts of a gene is predominantly influenced by alternate promoter usage. By overlapping brain-specific transcripts with the cell-type gene-markers in scBrainMap database, we found that 63% of the brain-specific transcripts were enriched in nonneuronal cell types, predominantly astrocytes followed by endothelial cells and oligodendrocytes. In addition, we found 61 brain cell-type marker genes encoding a total of 176 alternative transcripts as brain-specific and 22 alternative transcripts as testis-specific, highlighting the complex TSp and cell-type specific gene regulation and expression at isoform-level. TransTEx can be adopted to the analysis of bulk RNA-seq or scRNA-seq datasets to find tissue- and/or cell-type specific isoform-level gene markers. Availability and implementation TransTEx database: https://bmi.cewit.stonybrook.edu/transtexdb/ and the R package is available via GitHub: https://github.com/pallavisurana1/TransTEx. [ABSTRACT FROM AUTHOR]

Summary Most tools for normalizing NanoString gene expression data, apart from the default NanoString nCounter software, are R packages that focus on technical normalization and lack configurable parameters. However, content normalization is the most sensitive, experiment-specific, and relevant step to preprocess NanoString data. Currently this step requires the use of multiple tools and a deep understanding of data management by the researcher. We present GUANIN, a comprehensive normalization tool that integrates both new and well-established methods, offering a wide variety of options to introduce, filter, choose, and evaluate reference genes for content normalization. GUANIN allows the introduction of genes from an endogenous subset as reference genes, addressing housekeeping-related selection problems. It performs a specific and straightforward normalization approach for each experiment, using a wide variety of parameters with suggested default values. GUANIN provides a large number of informative output files that enable the iterative refinement of the normalization process. In terms of normalization, GUANIN matches or outperforms other available methods. Importantly, it allows researchers to interact comprehensively with the data preprocessing step without programming knowledge, thanks to its easy-to-use Graphical User Interface (GUI). Availability and implementation GUANIN can be installed with pip install GUANIN and it is available at https://pypi.org/project/guanin/. Source code, documentation, and case studies are available at https://github.com/julimontoto/guanin under the GPLv3 license. [ABSTRACT FROM AUTHOR]

Motivation Many types of networks, such as co-expression or ChIP-seq-based gene-regulatory networks, provide useful information for biomedical studies. However, they are often too full of connections and difficult to interpret, forming "indecipherable hairballs." Results To address this issue, we propose that a Bayesian network can summarize the core relationships between gene expression activities. This network, which we call the LatentDAG, is substantially simpler than conventional co-expression network and ChIP-seq networks (by two orders of magnitude). It provides clearer clusters, without extraneous cross-cluster connections, and clear separators between modules. Moreover, one can find a number of clear examples showing how it bridges the connection between steps in the transcriptional regulatory network and other networks (e.g. RNA-binding protein). In conjunction with a graph neural network, the LatentDAG works better than other biological networks in a variety of tasks, including prediction of gene conservation and clustering genes. Availability and implementation Code is available at https://github.com/gersteinlab/LatentDAG [ABSTRACT FROM AUTHOR]

Radiotherapy is a key treatment option for a wide variety of human tumors, employed either alone or alongside with other therapeutic interventions. Radiotherapy uses high-energy particles to destroy tumor cells, blocking their ability to divide and proliferate. The effectiveness of radiotherapy is due to genetic and epigenetic factors that determine how tumor cells respond to ionizing radiation. These factors contribute to the establishment of resistance to radiotherapy, which increases the risk of poor clinical prognosis of patients. Although the mechanisms by which tumor cells induce radioresistance are unclear, evidence points out several contributing factors including the overexpression of DNA repair systems, increased levels of reactive oxygen species, alterations in the tumor microenvironment, and enrichment of cancer stem cell populations. In this context, dysregulation of microRNAs or miRNAs, critical regulators of gene expression, may influence how tumors respond to radiation. There is increasing evidence that miRNAs may act as sensitizers or enhancers of radioresistance, regulating key processes such as the DNA damage response and the cell death signaling pathway. Furthermore, expression and activity of miRNAs have shown informative value in overcoming radiotherapy and long-term radiotoxicity, revealing their potential as biomarkers. In this review, we will discuss the molecular mechanisms associated with the response to radiotherapy and highlight the central role of miRNAs in regulating the molecular mechanisms responsible for cellular radioresistance. We will also review radio-miRs, radiotherapy-related miRNAs, either as sensitizers or enhancers of radioresistance that hold promise as biomarkers or pharmacological targets to sensitize radioresistant cells. [ABSTRACT FROM AUTHOR]

APOBEC proteins are cytidine deaminases that restrict the replication of viruses and transposable elements. Several members of the APOBEC3 family, APOBEC3A, APOBEC3B, and APOBEC3H-I, can access the nucleus and cause what is thought to be indiscriminate deamination of the genome, resulting in mutagenesis and genome instability. Although APOBEC3C is also present in the nucleus, the full scope of its deamination target preferences is unknown. By expressing human APOBEC3C in a yeast model system, I have defined the APOBEC3C mutation signature, as well as the preferred genome features of APOBEC3C targets. The APOBEC3C mutation signature is distinct from those of the known cancer genome mutators APOBEC3A and APOBEC3B. APOBEC3C produces DNA strand-coordinated mutation clusters, and APOBEC3C mutations are enriched near the transcription start sites of active genes. Surprisingly, APOBEC3C lacks the bias for the lagging strand of DNA replication that is seen for APOBEC3A and APOBEC3B. The unique preferences of APOBEC3C constitute a mutation profile that will be useful in defining sites of APOBEC3C mutagenesis in human genomes. [ABSTRACT FROM AUTHOR]

The T-box (Tbx) proteins have a 180–230 amino acid DNA-binding domain, first reported in the Brachyury (T) protein. They are highly conserved among metazoans. They regulate a multitude of cellular functions in development and disease. Here, we report posttranscriptional and translational regulation of midline (mid), a Tbx member in Drosophila. We found that the 3′UTR of mid has mRNA degradation elements and AT-rich sequences. In Schneider S2 cells, mid- mRNA could be detected only when the transgene was without the 3′UTR. Similarly, the 3′UTR linked to the Renilla luciferase reporter significantly reduced the activity of the Luciferase, whereas deleting only the degradation elements from the 3′UTR resulted in reduced activity, but not as much. Overexpression of mid in MP2, an embryonic neuroblast, showed no significant difference in the levels of mid- mRNA between the 2 transgenes, with and without the 3′UTR, indicating the absence of posttranscriptional regulation of mid in MP2. Moreover, while elevated mid- RNA was detected in MP2 in nearly all hemisegments, only a fifth of those hemisegments had elevated levels of the protein. Overexpression of the 2 transgenes resulted in MP2-lineage defects at about the same frequency. These results indicate a translational/posttranslational regulation of mid in MP2. The regulation of ectopically expressed mid in the wing imaginal disc was complex. In the wing disc, where mid is not expressed, the ectopic expression of the transgene lacking the 3′UTR had a higher level of mid- RNA and the protein had a stronger phenotypic effect. These results indicate that the 3′UTR can subject mid -mRNA to degradation in a cell- and tissue-specific manner. We further report a balancer-mediated transgenerational modifier effect on the expression and gain of function effects of the 2 transgenes. [ABSTRACT FROM AUTHOR]

In Caenorhabditis elegans , expanded families of divergent Hedgehog-related and patched-related proteins promote numerous processes ranging from epithelial and sense organ development to pathogen responses to cuticle shedding during the molt cycle. The molecular functions of these proteins have been mysterious since nematodes lack a canonical Hedgehog signaling pathway. Here we show that Hedgehog-related proteins are components of the cuticle and precuticle apical extracellular matrices that coat, shape, and protect external epithelia. Of four Hedgehog-related proteins imaged, two (GRL-2 and GRL-18) stably associated with the cuticles of specific tubes and two (GRL-7 and WRT-10) labeled precuticle substructures such as furrows or alae. We found that wrt-10 mutations disrupt cuticle alae ridges, consistent with a structural role in matrix organization. We hypothesize that most nematode Hedgehog-related proteins are apical extracellular matrix components, a model that could explain many of the reported functions for this family. These results highlight ancient connections between Hedgehog proteins and the extracellular matrix and suggest that any signaling roles of C. elegans Hedgehog-related proteins will be intimately related to their matrix association. [ABSTRACT FROM AUTHOR]

The current toolkit for genetic manipulation in the model animal Drosophila melanogaster is extensive and versatile but not without its limitations. Here, we report a powerful and heritable method to knockdown gene expression in D. melanogaster using the self-cleaving N79 hammerhead ribozyme, a modification of a naturally occurring ribozyme found in the parasite Schistosoma mansoni. A 111-bp ribozyme cassette, consisting of the N79 ribozyme surrounded by insulating spacer sequences, was inserted into 4 independent long noncoding RNA genes as well as the male-specific splice variant of doublesex using scarless CRISPR/Cas9-mediated genome editing. Ribozyme-induced RNA cleavage resulted in robust destruction of 3′ fragments typically exceeding 90%. Single molecule RNA fluorescence in situ hybridization results suggest that cleavage and destruction can even occur for nascent transcribing RNAs. Knockdown was highly specific to the targeted RNA, with no adverse effects observed in neighboring genes or the other splice variants. To control for potential effects produced by the simple insertion of 111 nucleotides into genes, we tested multiple catalytically inactive ribozyme variants and found that a variant with scrambled N79 sequence best recapitulated natural RNA levels. Thus, self-cleaving ribozymes offer a novel approach for powerful gene knockdown in Drosophila , with potential applications for the study of noncoding RNAs, nuclear-localized RNAs, and specific splice variants of protein-coding genes. [ABSTRACT FROM AUTHOR]

How species thrive in a wide range of environments is a major focus of evolutionary biology. For many species, limited genetic diversity or gene flow among habitats means that phenotypic plasticity must play an important role in their capacity to tolerate environmental heterogeneity and to colonize new habitats. However, we have a limited understanding of the molecular components that govern plasticity in ecologically relevant phenotypes. We examined this hypothesis in a spider species (Stegodyphus dumicola) with extremely low species-wide genetic diversity that nevertheless occupies a broad range of thermal environments. We determined phenotypic responses to temperature stress in individuals from four climatic zones using common garden acclimation experiments to disentangle phenotypic plasticity from genetic adaptations. Simultaneously, we created data sets on multiple molecular modalities: the genome, the transcriptome, the methylome, the metabolome, and the bacterial microbiome to determine associations with phenotypic responses. Analyses of phenotypic and molecular associations reveal that acclimation responses in the transcriptome and metabolome correlate with patterns of phenotypic plasticity in temperature tolerance. Surprisingly, genes whose expression seemed to be involved in plasticity in temperature tolerance were generally highly methylated contradicting the idea that DNA methylation stabilizes gene expression. This suggests that the function of DNA methylation in invertebrates varies not only among species but also among genes. The bacterial microbiome was stable across the acclimation period; combined with our previous demonstrations that the microbiome is temporally stable in wild populations, this is convincing evidence that the microbiome does not facilitate plasticity in temperature tolerance. Our results suggest that population-specific variation in temperature tolerance among acclimation temperatures appears to result from the evolution of plasticity in mainly gene expression. [ABSTRACT FROM AUTHOR]

The shift from outcrossing to self-fertilization is one of the main evolutionary transitions in plants and has broad effects on evolutionary trajectories. In Brassicaceae, the ability to inhibit self-fertilization is controlled by 2 genes, SCR and SRK , tightly linked within the S -locus. A series of small non-coding RNAs also encoded within the S -locus regulates the transcriptional activity of SCR alleles, resulting in a linear dominance hierarchy between them. In Brassicaceae, natural allopolyploid species are often self-compatible (SC) even when one of the progenitor species is self-incompatible, but the reason why polyploid lineages tend to lose self-incompatibility (SI) and the timing of the loss of SI (immediately after ancestral hybridization between the progenitor species, or at a later stage after the formation of allopolyploid lineages) have generally remained elusive. We used a series of synthetic diploid and tetraploid hybrids obtained between self-fertilizing Capsella orientalis and outcrossing Capsella grandiflora to test whether the breakdown of SI could be observed immediately after hybridization, and whether the occurrence of SC phenotypes could be explained by the dominance interactions between S -haplotypes inherited from the parental lineages. We used RNA-sequencing data from young inflorescences to measure allele-specific expression of the SCR gene and infer dominance interactions in the synthetic hybrids. We then evaluated the seed set from autonomous self-pollination in the synthetic hybrids. Our results demonstrate that self-compatibility of the hybrids depends on the relative dominance between S -alleles inherited from the parental species, confirming that SI can be lost instantaneously upon formation of the ancestral allopolyploid lineage. They also confirm that the epigenetic regulation that controls dominance interactions between S -alleles can function between subgenomes in allopolyploids. Together, our results illustrate how a detailed knowledge of the mechanisms controlling SI can illuminate our understanding of the patterns of co-variation between the mating system and changes in ploidy. [ABSTRACT FROM AUTHOR]

Candida albicans is a major fungal pathogen of humans that can cause serious systemic infections in vulnerable immunocompromised populations. One of its virulence attributes is its capacity to transition between yeast and filamentous morphologies, but our understanding of this process remains incomplete. Here, we analyzed data from a functional genomic screen performed with the C. albicans Gene Replacement And Conditional Expression collection to identify genes crucial for morphogenesis in host-relevant conditions. Through manual scoring of microscopy images coupled with analysis of each image using a deep learning-based method termed Candescence, we identified 307 genes important for filamentation in tissue culture medium at 37°C with 5% CO2. One such factor was orf19.5963 , which is predicted to encode the prenyltransferase Nus1 based on sequence homology to Saccharomyces cerevisiae. We further showed that Nus1 and its predicted interacting partner Rer2 are important for filamentation in multiple liquid filament-inducing conditions as well as for wrinkly colony formation on solid agar. Finally, we highlight that Nus1 and Rer2 likely govern C. albicans morphogenesis due to their importance in intracellular trafficking, as well as maintaining lipid homeostasis. Overall, this work identifies Nus1 and Rer2 as important regulators of C. albicans filamentation and highlights the power of functional genomic screens in advancing our understanding of gene function in human fungal pathogens. [ABSTRACT FROM AUTHOR]

Accurate chromosome segregation during meiosis requires the maintenance of sister chromatid cohesion, initially established during premeiotic S phase. In human oocytes, DNA replication and cohesion establishment occur decades before chromosome segregation and deterioration of meiotic cohesion is one factor that leads to increased segregation errors as women age. Our previous work led us to propose that a cohesion rejuvenation program operates to establish new cohesive linkages during meiotic prophase in Drosophila oocytes and depends on the cohesin loader Nipped-B and the cohesion establishment factor Eco. In support of this model, we recently demonstrated that chromosome-associated cohesin turns over extensively during meiotic prophase and failure to load cohesin onto chromosomes after premeiotic S phase results in arm cohesion defects in Drosophila oocytes. To identify proteins required for prophase cohesion rejuvenation but not S phase establishment, we conducted a Gal4-UAS inducible RNAi screen that utilized two distinct germline drivers. Using this strategy, we identified 29 gene products for which hairpin expression during meiotic prophase, but not premeiotic S phase, significantly increased segregation errors. Prophase knockdown of Brahma or Pumilio, two positives with functional links to the cohesin loader, caused a significant elevation in the missegregation of recombinant homologs, a phenotype consistent with premature loss of arm cohesion. Moreover, fluorescence in situ hybridization confirmed that Brahma, Pumilio, and Nipped-B are required during meiotic prophase for the maintenance of arm cohesion. Our data support the model that Brahma and Pumilio regulate Nipped-B-dependent cohesin loading during rejuvenation. Future analyses will better define the mechanism(s) that govern meiotic cohesion rejuvenation and whether additional prophase-specific positives function in this process. [ABSTRACT FROM AUTHOR]

Body size is a fundamental trait that drives multiple evolutionary and ecological patterns. Caenorhabditis inopinata is a fig-associated nematode that is exceptionally large relative to other members of the genus, including Caenorhabditis elegans. We previously showed that C. inopinata is large primarily due to postembryonic cell size expansion that occurs during the larval-to-adult transition. Here, we describe gene expression patterns in C. elegans and C. inopinata throughout this developmental period to understand the transcriptional basis of body size change. We performed RNA-seq in both species across the L3, L4, and adult stages. Most genes are differentially expressed across all developmental stages, consistent with C. inopinata 's divergent ecology and morphology. We also used a model comparison approach to identify orthologues with divergent dynamics across this developmental period between the 2 species. This included genes connected to neurons, behavior, stress response, developmental timing, and small RNA/chromatin regulation. Multiple hypodermal collagens were also observed to harbor divergent developmental dynamics across this period, and genes important for molting and body morphology were also detected. Genes associated with transforming growth factor β signaling revealed idiosyncratic and unexpected transcriptional patterns given their role in body size regulation in C. elegans. This widespread transcriptional divergence between these species is unexpected and maybe a signature of the ecological and morphological divergence of C. inopinata. Alternatively, transcriptional turnover may be the rule in the Caenorhabditis genus, indicative of widespread developmental system drift among species. This work lays the foundation for future functional genetic studies interrogating the bases of body size evolution in this group. [ABSTRACT FROM AUTHOR]

Background Surgical stress may lead to postsurgical hypercoagulability, endothelial dysfunction and systemic inflammation, which can impact on patient recovery. Remote ischaemic preconditioning is a procedure that activates the body's endogenous defences against ischaemia and reperfusion injury. Studies have suggested that remote ischaemic preconditioning has antithrombotic, antioxidative and anti-inflammatory effects. The hypothesis was that remote ischaemic preconditioning reduces surgery-induced systemic stress response. Method During a 24-month period (2019–2021), adult patients undergoing subacute laparoscopic cholecystectomy due to acute cholecystitis were randomized to remote ischaemic preconditioning or control. Remote ischaemic preconditioning was performed less than 4 h before surgery on the upper arm. It consisted of four cycles of 5 min ischaemia and 5 min reperfusion. The gene expression of 750 genes involved in inflammatory processes, oxidative stress and endothelial function was investigated preoperatively and 2–4 h after surgery in both groups. In addition, changes in 20 inflammation- and vascular trauma–associated proteins were assessed preoperatively, 2–4 h after surgery and 24 h after surgery. Results A total of 60 patients were randomized. There were no statistically significant differences in gene expression 2–4 h after surgery between the groups (P > 0.05). Remote ischaemic preconditioning did not affect concentrations of circulating proteins up to 24 h after surgery (P > 0.05). Conclusion The study did not demonstrate any effect of remote ischaemic preconditioning on expression levels of the chosen genes or in circulating immunological cytokines and vascular trauma–associated proteins up to 24 h after subacute laparoscopic cholecystectomy in patients with acute cholecystitis. [ABSTRACT FROM AUTHOR]

The molecular underpinnings and consequences of cycles of whole-genome duplication (WGD) and subsequent gene loss through subgenome fractionation remain largely elusive. Endogenous drivers, such as transposable elements (TEs), have been postulated to shape genome-wide dominance and biased fractionation, leading to a conserved least-fractionated (LF) subgenome and a degenerated most-fractionated (MF) subgenome. In contrast, the role of exogenous factors, such as those induced by environmental stresses, has been overlooked. In this study, a chromosome-scale assembly of the alpine buckler mustard (Biscutella laevigata ; Brassicaceae) that underwent a WGD event about 11 million years ago is coupled with transcriptional responses to heat, cold, drought, and herbivory to assess how gene expression is associated with differential gene retention across the MF and LF subgenomes. Counteracting the impact of TEs in reducing the expression and retention of nearby genes across the MF subgenome, dosage balance is highlighted as a main endogenous promoter of the retention of duplicated gene products under purifying selection. Consistent with the "turn a hobby into a job" model, about one-third of environment-responsive duplicates exhibit novel expression patterns, with one copy typically remaining conditionally expressed, whereas the other copy has evolved constitutive expression, highlighting exogenous factors as a major driver of gene retention. Showing uneven patterns of fractionation, with regions remaining unbiased, but with others showing high bias and significant enrichment in environment-responsive genes, this mesopolyploid genome presents evolutionary signatures consistent with an interplay of endogenous and exogenous factors having driven gene content following WGD-fractionation cycles. [ABSTRACT FROM AUTHOR]

Background Patients with intrahepatic cholangiocarcinoma (ICC) are prone to recurrence and poor survival. Targeted therapy related to isocitrate dehydrogenase (IDH) is an extremely important treatment. IDH1 and IDH2 mutations are generally thought to have similar effects on the tumor landscape. However, it is doubtful whether these 2 mutations have exactly the same effects on tumor cells and the tumor microenvironment. Methods All collected tumor samples were subjected to simultaneous whole-exon sequencing and proteome sequencing. Results IDH1 mutations accounted for 12.2%, and IDH2 mutations accounted for 5.5%, all missense mutations. Tumors with IDH mutations had lower proportions of KRAS and TP53 mutations. Mutated genes were obviously enriched in the kinase pathway in the tumors with IDH2 mutations. The signaling pathways were mainly enriched in the activation of cellular metabolic activities and an increase of inhibitory immune cells in the tumors with IDH mutations. Moreover, tumors had unique enrichment in DNA repair in IDH1 mutants and secretion of biological molecules in IDH2 mutants. Inhibitory immune cells might be more prominent in IDH2 mutants, and the expression of immune checkpoints PVR and HLA-DQB1 was more prominent in IDH1 mutants. IDH mutants were more related to metabolism-related and inflammation-immune response clusters, and some belonged to the DNA replication and repair cluster. Conclusions These results revealed the differential IDH1 and IDH2 mutation-related landscapes, and we have provided an important reference database to guide ICC treatment. [ABSTRACT FROM AUTHOR]

HER2, encoded by the ERBB2 gene, is an important druggable driver of human cancer gaining increasing importance as a therapeutic target in urothelial carcinoma (UC). The genomic underpinnings of HER2 overexpression in ERBB2 nonamplified UC are poorly defined. To address this knowledge gap, we investigated 172 UC tumors from patients treated at the University of California San Francisco, using immunohistochemistry and next-generation sequencing. We found that GATA3 and PPARG copy number gains individually predicted HER2 protein expression independently of ERBB2 amplification. To validate these findings, we interrogated the Memorial Sloan Kettering/The Cancer Genome Atlas (MSK/TCGA) dataset and found that GATA3 and PPARG copy number gains individually predicted ERBB2 mRNA expression independently of ERBB2 amplification. Our findings reveal a potential link between the luminal marker HER2 and the key transcription factors GATA3 and PPARG in UC and highlight the utility of examining GATA3 and PPARG copy number states to identify UC tumors that overexpress HER2 in the absence of ERBB2 amplification. In summary, we found that an increase in copy number of GATA3 and PPARG was independently associated with higher ERBB2 expression in patient samples of UC. This finding provides a potential explanation for HER2 overexpression in UC tumors without ERBB2 amplification and a way to identify these tumors for HER2-targeted therapies. [ABSTRACT FROM AUTHOR]

Background There are limited conventional chemotherapy options for biliary tract cancers (BTCs), a heterogenous group of lethal, rare malignancies. The receptor tyrosine kinase (RTK) is closely associated with the progression of human malignancies through the regulation of cell cycle. Overexpression or amplification of RTKs has been investigated as a potential biomarker and therapeutic target in BTC; herein, we investigate the value of such interventions. Materials and Methods Overexpression of RTK proteins was examined by immunohistochemistry in 193 BTC samples, of which 137 were gallbladder carcinoma, 29 were perihilar cholangiocarcinoma, and 27 were intrahepatic cholangiocarcinoma. Silver in situ hybridization of MET and HER2 was performed to assess gene amplification. Results In the entire cancer group, gallbladder, perihilar, and intrahepatic, MET amplification rates were 15.7%, 19.0%, 3.4%, and 14.8%, respectively, and of HER2 amplification rates were 22.4%, 27.2%, 17.2%, and 3.7%, respectively. MET and HER2 protein expressions were significantly correlated with their gene amplification status. RTKs were significantly associated with adverse clinicopathologic features such as advanced pT category and lymph node metastasis. Overall survival was significantly shorter in MET- amplified (P  = .024) and EGFR-overexpressed cases (P  = .045). Recurrence-free survival was significantly correlated with HER2 -amplified (P  = .038) and EGFR-overexpressed cases (P  = .046) in all patient groups. Overall and recurrence-free survival were significantly shorter in patients who were double positive for HER2 and EGFR. Conclusion Our data suggested that MET, HER2, and EGFR might be potential therapeutic targets and that their co-expression is a strong prognostic factor for BTCs. [ABSTRACT FROM AUTHOR]

Background Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations. Results Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources. [ABSTRACT FROM AUTHOR]

Motivation Spatially resolved single-cell transcriptomics have provided unprecedented insights into gene expression in situ , particularly in the context of cell interactions or organization of tissues. However, current technologies for profiling spatial gene expression at single-cell resolution are generally limited to the measurement of a small number of genes. To address this limitation, several algorithms have been developed to impute or predict the expression of additional genes that were not present in the measured gene panel. Current algorithms do not leverage the rich spatial and gene relational information in spatial transcriptomics. To improve spatial gene expression predictions, we introduce Spatial Propagation and Reinforcement of Imputed Transcript Expression (SPRITE) as a meta-algorithm that processes predictions obtained from existing methods by propagating information across gene correlation networks and spatial neighborhood graphs. Results SPRITE improves spatial gene expression predictions across multiple spatial transcriptomics datasets. Furthermore, SPRITE predicted spatial gene expression leads to improved clustering, visualization, and classification of cells. SPRITE can be used in spatial transcriptomics data analysis to improve inferences based on predicted gene expression. Availability and implementation The SPRITE software package is available at https://github.com/sunericd/SPRITE. Code for generating experiments and analyses in the manuscript is available at https://github.com/sunericd/sprite-figures-and-analyses. [ABSTRACT FROM AUTHOR]

Motivation In many biomedical applications, we are confronted with paired groups of samples, such as treated versus control. The aim is to detect discriminating features, i.e. biomarkers, based on high-dimensional (omics-) data. This problem can be phrased more generally as a two-sample problem requiring statistical significance testing to establish differences , and interpretations to identify distinguishing features. The multivariate maximum mean discrepancy (MMD) test quantifies group-level differences, whereas statistically significantly associated features are usually found by univariate feature selection. Currently, few general-purpose methods simultaneously perform multivariate feature selection and two-sample testing. Results We introduce a sparse, interpretable, and optimized MMD test (SpInOpt-MMD) that enables two-sample testing and feature selection in the same experiment. SpInOpt-MMD is a versatile method and we demonstrate its application to a variety of synthetic and real-world data types including images, gene expression measurements, and text data. SpInOpt-MMD is effective in identifying relevant features in small sample sizes and outperforms other feature selection methods such as SHapley Additive exPlanations and univariate association analysis in several experiments. Availability and implementation The code and links to our public data are available at https://github.com/BorgwardtLab/spinoptmmd. [ABSTRACT FROM AUTHOR]

Summary Drug response is conventionally measured at the cell level, often quantified by metrics like IC50. However, to gain a deeper understanding of drug response, cellular outcomes need to be understood in terms of pathway perturbation. This perspective leads us to recognize a challenge posed by the gap between two widely used large-scale databases, LINCS L1000 and GDSC, measuring drug response at different levels—L1000 captures information at the gene expression level, while GDSC operates at the cell line level. Our study aims to bridge this gap by integrating the two databases through transfer learning, focusing on condition-specific perturbations in gene interactions from L1000 to interpret drug response integrating both gene and cell levels in GDSC. This transfer learning strategy involves pretraining on the transcriptomic-level L1000 dataset, with parameter-frozen fine-tuning to cell line-level drug response. Our novel condition-specific gene–gene attention (CSG 2 A) mechanism dynamically learns gene interactions specific to input conditions, guided by both data and biological network priors. The CSG 2 A network, equipped with transfer learning strategy, achieves state-of-the-art performance in cell line-level drug response prediction. In two case studies, well-known mechanisms of drugs are well represented in both the learned gene–gene attention and the predicted transcriptomic profiles. This alignment supports the modeling power in terms of interpretability and biological relevance. Furthermore, our model's unique capacity to capture drug response in terms of both pathway perturbation and cell viability extends predictions to the patient level using TCGA data, demonstrating its expressive power obtained from both gene and cell levels. Availability and implementation The source code for the CSG 2 A network is available at https://github.com/eugenebang/CSG2A. [ABSTRACT FROM AUTHOR]

Spatial transcripome (ST) profiling can reveal cells' structural organizations and functional roles in tissues. However, deciphering the spatial context of gene expressions in ST data is a challenge—the high-order structure hiding in whole transcriptome space over 2D/3D spatial coordinates requires modeling and detection of interpretable high-order elements and components for further functional analysis and interpretation. This paper presents a new method GraphTucker—graph-regularized Tucker tensor decomposition for learning high-order factorization in ST data. GraphTucker is based on a nonnegative Tucker decomposition algorithm regularized by a high-order graph that captures spatial relation among spots and functional relation among genes. In the experiments on several Visium and Stereo-seq datasets, the novelty and advantage of modeling multiway multilinear relationships among the components in Tucker decomposition are demonstrated as opposed to the Canonical Polyadic Decomposition and conventional matrix factorization models by evaluation of detecting spatial components of gene modules, clustering spatial coefficients for tissue segmentation and imputing complete spatial transcriptomes. The results of visualization show strong evidence that GraphTucker detect more interpretable spatial components in the context of the spatial domains in the tissues. Availability and implementation https://github.com/kuanglab/GraphTucker. [ABSTRACT FROM AUTHOR]

Motivation Genetic perturbations (e.g. knockouts, variants) have laid the foundation for our understanding of many diseases, implicating pathogenic mechanisms and indicating therapeutic targets. However, experimental assays are fundamentally limited by the number of measurable perturbations. Computational methods can fill this gap by predicting perturbation effects under novel conditions, but accurately predicting the transcriptional responses of cells to unseen perturbations remains a significant challenge. Results We address this by developing a novel attention-based neural network, AttentionPert, which accurately predicts gene expression under multiplexed perturbations and generalizes to unseen conditions. AttentionPert integrates global and local effects in a multi-scale model, representing both the nonuniform system-wide impact of the genetic perturbation and the localized disturbance in a network of gene–gene similarities, enhancing its ability to predict nuanced transcriptional responses to both single and multi-gene perturbations. In comprehensive experiments, AttentionPert demonstrates superior performance across multiple datasets outperforming the state-of-the-art method in predicting differential gene expressions and revealing novel gene regulations. AttentionPert marks a significant improvement over current methods, particularly in handling the diversity of gene perturbations and in predicting out-of-distribution scenarios. Availability and implementation Code is available at https://github.com/BaiDing1234/AttentionPert. [ABSTRACT FROM AUTHOR]

Summary Artificial intelligence (AI) is increasingly used in genomics research and practice, and generative AI has garnered significant recent attention. In clinical applications of generative AI, aspects of the underlying datasets can impact results, and confounders should be studied and mitigated. One example involves the facial expressions of people with genetic conditions. Stereotypically, Williams (WS) and Angelman (AS) syndromes are associated with a "happy" demeanor, including a smiling expression. Clinical geneticists may be more likely to identify these conditions in images of smiling individuals. To study the impact of facial expression, we analyzed publicly available facial images of approximately 3500 individuals with genetic conditions. Using a deep learning (DL) image classifier, we found that WS and AS images with non-smiling expressions had significantly lower prediction probabilities for the correct syndrome labels than those with smiling expressions. This was not seen for 22q11.2 deletion and Noonan syndromes, which are not associated with a smiling expression. To further explore the effect of facial expressions, we computationally altered the facial expressions for these images. We trained HyperStyle, a GAN-inversion technique compatible with StyleGAN2, to determine the vector representations of our images. Then, following the concept of InterfaceGAN, we edited these vectors to recreate the original images in a phenotypically accurate way but with a different facial expression. Through online surveys and an eye-tracking experiment, we examined how altered facial expressions affect the performance of human experts. We overall found that facial expression is associated with diagnostic accuracy variably in different genetic conditions. [ABSTRACT FROM AUTHOR]

Motivation Profiling of gene expression and chromatin accessibility by single-cell multi-omics approaches can help to systematically decipher how transcription factors (TFs) regulate target gene expression via cis -region interactions. However, integrating information from different modalities to discover regulatory associations is challenging, in part because motif scanning approaches miss many likely TF binding sites. Results We develop REUNION, a framework for predicting genome-wide TF binding and cis -region-TF-gene "triplet" regulatory associations using single-cell multi-omics data. The first component of REUNION, Unify, utilizes information theory-inspired complementary score functions that incorporate TF expression, chromatin accessibility, and target gene expression to identify regulatory associations. The second component, Rediscover, takes Unify estimates as input for pseudo semi-supervised learning to predict TF binding in accessible genomic regions that may or may not include detected TF motifs. Rediscover leverages latent chromatin accessibility and sequence feature spaces of the genomic regions, without requiring chromatin immunoprecipitation data for model training. Applied to peripheral blood mononuclear cell data, REUNION outperforms alternative methods in TF binding prediction on average performance. In particular, it recovers missing region-TF associations from regions lacking detected motifs, which circumvents the reliance on motif scanning and facilitates discovery of novel associations involving potential co-binding transcriptional regulators. Newly identified region-TF associations, even in regions lacking a detected motif, improve the prediction of target gene expression in regulatory triplets, and are thus likely to genuinely participate in the regulation. Availability and implementation All source code is available at https://github.com/yangymargaret/REUNION. [ABSTRACT FROM AUTHOR]

Motivation Cell–cell interactions (CCIs) consist of cells exchanging signals with themselves and neighboring cells by expressing ligand and receptor molecules and play a key role in cellular development, tissue homeostasis, and other critical biological functions. Since direct measurement of CCIs is challenging, multiple methods have been developed to infer CCIs by quantifying correlations between the gene expression of the ligands and receptors that mediate CCIs, originally from bulk RNA-sequencing data and more recently from single-cell or spatially resolved transcriptomics (SRT) data. SRT has a particular advantage over single-cell approaches, since ligand–receptor correlations can be computed between cells or spots that are physically close in the tissue. However, the transcript counts of individual ligands and receptors in SRT data are generally low, complicating the inference of CCIs from expression correlations. Results We introduce Copulacci, a count-based model for inferring CCIs from SRT data. Copulacci uses a Gaussian copula to model dependencies between the expression of ligands and receptors from nearby spatial locations even when the transcript counts are low. On simulated data, Copulacci outperforms existing CCI inference methods based on the standard Spearman and Pearson correlation coefficients. Using several real SRT datasets, we show that Copulacci discovers biologically meaningful ligand–receptor interactions that are lowly expressed and undiscoverable by existing CCI inference methods. Availability and implementation Copulacci is implemented in Python and available at https://github.com/raphael-group/copulacci. [ABSTRACT FROM AUTHOR]

Summary Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the high cost and the complexity of the experimental protocol. We present a deep learning framework, scGrapHiC, that predicts pseudo-bulk scHi-C contact maps using pseudo-bulk scRNA-seq data. Specifically, scGrapHiC performs graph deconvolution to extract genome-wide single-cell interactions from a bulk Hi-C contact map using scRNA-seq as a guiding signal. Our evaluations show that scGrapHiC, trained on seven cell-type co-assay datasets, outperforms typical sequence encoder approaches. For example, scGrapHiC achieves a substantial improvement of 23.2 % in recovering cell-type-specific Topologically Associating Domains over the baselines. It also generalizes to unseen embryo and brain tissue samples. scGrapHiC is a novel method to generate cell-type-specific scHi-C contact maps using widely available genomic signals that enables the study of cell-type-specific chromatin interactions. Availability and implementation The GitHub link: https://github.com/rsinghlab/scGrapHiC contains the source code of scGrapHiC and associated scripts to preprocess publicly available datasets to produce the results and visualizations we have discuss in this manuscript. [ABSTRACT FROM AUTHOR]