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A cell's fate involves transitions among its various states, each defined by a distinct gene expression profile governed by the topology of gene regulatory networks, which are affected by 3D genome organization. Here, we develop thermodynamic models to determine the fate of a malignant cell as governed by the tumor suppressor p53 signaling network, taking into account long-range chromatin interactions in the mean-field approximation. The tumor suppressor p53 responds to stress by selectively triggering one of the potential transcription programs that influence many layers of cell signaling. These range from p53 phosphorylation to modulation of its DNA binding affinity, phase separation phenomena, and internal connectivity among cell fate genes. We use the minimum free energy of the system as a fundamental property of biological networks that influences the connection between the gene network topology and the state of the cell. We constructed models based on network topology and equilibrium thermodynamics. Our modeling shows that the binding of phosphorylated p53 to promoters of target genes can have properties of a first order phase transition. We apply our model to cancer cell lines ranging from breast cancer (MCF-7), colon cancer (HCT116), and leukemia (K562), with each one characterized by a specific network topology that determines the cell fate. Our results clarify the biological relevance of these mechanisms and suggest that they represent flexible network designs for switching between developmental decisions. [ABSTRACT FROM AUTHOR]

Resistance training is used to combat skeletal muscle function decline in older adults. Few studies have been designed specific for females, resulting in very limited treatment options for skeletal muscle atrophy in aging women. Here, we analyzed the gene expression profiles of skeletal muscle samples from sedentary young women, sedentary older women, and resistance-trained older women, using microarray data from public database. A total of 45 genes that were differentially expressed during female muscle aging and reversed by resistance training were identified. Functional and pathway enrichment analysis, protein–protein interaction network analysis, and receiver operating characteristic analysis were performed to reveal the key genes and pathways involved in the effects of resistance training on female muscle aging. The collagen genes COL1A1, COL3A1, and COL4A1 were identified important regulators of female muscle aging and resistance training, by modulating multiple signaling pathways, such as PI3 kinase-Akt signaling, focal adhesions, extracellular matrix-receptor interactions, and relaxin signaling. Interestingly, the expression of CDKN1A and TP63 were increased during aging, and further upregulated by resistance training in older women, suggesting they may negatively affect resistance training outcomes. Our findings provide novel insights into the molecular mechanisms of resistance training on female muscle aging and identify potential biomarkers and targets for clinical intervention. [ABSTRACT FROM AUTHOR]

The dog, Canis lupus familiaris, is an important model for studying human diseases. Unlike many model organisms, the dog genome has a comparatively poor functional annotation, which hampers gene discovery for development, morphology, disease, and behavior. To fill this gap, we established a comprehensive tissue biobank for both the dog and wolf samples. The biobank consists of 5485 samples representing 132 tissues from 13 dogs, 12 dog embryos, and 24 wolves. In a subset of 100 tissues from nine dogs and 12 embryos, we characterized gene expression activity for each promoter, including alternative and novel, i.e., previously not annotated, promoter regions, using the 5 targeting RNA sequencing technology STRT2-seq. We identified over 100,000 promoter region candidates in the recent canine genome assembly, CanFam4, including over 45,000 highly reproducible sites with gene expression and respective tissue enrichment levels. We provide a promoter and gene expression atlas with interactive, open data resources, including a data coordination center and genome browser track hubs. We demonstrated the applicability of Dog Genome Annotation (DoGA) data and resources using multiple examples spanning canine embryonic development, morphology and behavior, and diseases across species.

Torpor encompasses diverse adaptations to extreme environmental stressors such as hibernation, aestivation, brumation, and daily torpor. Here we introduce StrokeofGenus, an analytic pipeline that identifies distinct transcriptomic states and shared gene expression patterns across studies, tissues, and species. We use StrokeofGenus to study multiple and diverse forms of torpor from publicly-available RNA-seq datasets that span eight species and two classes. We identify three transcriptionally distinct states during the cycle of heterothermia: euthermia, torpor, and interbout arousal. We also identify torpor-specific gene expression patterns that are shared both across tissues and between species with over three hundred million years of evolutionary divergence. We further demonstrate the general sharing of gene expression patterns in multiple forms of torpor, implying a common evolutionary origin for this process. Although here we apply StrokeofGenus to analysis of torpor, it can be used to interrogate any other complex physiological processes defined by transient transcriptomic states.

Obesity confers risk for cardiovascular disease and vascular dementia. However, genomic alterations modulated by obesity in endothelial cells in the brain and their relationship to other neurovascular unit (NVU) cells are unknown. We performed single nuclei RNA sequencing (snRNAseq) of the NVU (endothelial cells, astrocytes, microglia, and neurons) from the hippocampus of obese (ob/ob) and wild-type (WT) male mice to characterize obesity-induced transcriptomic changes in a key brain memory center and assessed blood-brain barrier permeability (BBB) by gadolinium-enhanced magnetic resonance imaging (MRI). Ob/ob mice displayed obesity, hyperinsulinemia, and impaired glucose tolerance. snRNAseq profiled 14 distinct cell types and 32 clusters within the hippocampus of ob/ob and WT mice and uncovered differentially expressed genes (DEGs) in all NVU cell types, namely, 4462 in neurons, 1386 in astrocytes, 125 in endothelial cells, and 154 in microglia. Gene ontology analysis identified important biological processes such as angiogenesis in endothelial cells and synaptic trafficking in neurons. Cellular pathway analysis included focal adhesion and insulin signaling, which were common to all NVU cell types. Correlation analysis revealed significant positive correlations between endothelial cells and other NVU cell types. Differentially expressed long non-coding RNAs (lncRNAs) were observed in cells of the NVU-affecting pathways such as TNF and mTOR. BBB permeability showed a trend toward increased signal intensity in ob/ob mice. Taken together, our study provides in-depth insight into the molecular mechanisms underlying cognitive dysfunction in obesity and may have implications for therapeutic gene targeting.

UNLABELLED: Microorganisms with simplified genomes represent interesting cell chassis for systems and synthetic biology. However, genome reduction can lead to undesired traits, such as decreased growth rate and metabolic imbalances. To investigate the impact of genome reduction on Escherichia coli strain DGF-298, a strain in which ~ 36% of the genome has been removed, we reconstructed a strain-specific metabolic model (iAC1061), investigated the regulation of gene expression using iModulon-based transcriptome analysis, and performed adaptive laboratory evolution to let the strain correct potential imbalances that arose during its simplification. The model notably predicted that the removal of all three key pathways for glycolaldehyde disposal in this microorganism would lead to a metabolic bottleneck through folate starvation. Glycolaldehyde is also known to cause self-generation of reactive oxygen species, as evidenced by the increased expression of oxidative stress resistance genes in the SoxS iModulon. The reintroduction of the aldA gene, responsible for one native glycolaldehyde disposal route, alleviated the constitutive oxidative stress response. Our results suggest that systems-level approaches and adaptive laboratory evolution have additive benefits when trying to repair and optimize genome-engineered strains. IMPORTANCE: Genomic streamlining can be employed in model organisms to reduce complexity and enhance strain predictability. One of the most striking examples is the bacterial strain Escherichia coli DGF-298, notable for having over one-third of its genome deleted. However, such extensive genome modifications raise the question of how similar this simplified cell remains when compared with its parent, and what are the possible unintended consequences of this simplification. In this study, we used metabolic modeling along with iModulon-based transcriptomic analysis in different growth conditions to assess the impact of genome reduction on metabolism and gene regulation. We observed little impact of genomic reduction on the regulatory network of E. coli DGF-298 and identified a potential metabolic bottleneck leading to the constitutive activity of the SoxS iModulon. We then leveraged the models predictions to successfully restore SoxS activity to the basal level.

From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synthetic data generated from mathematical modeling. We demonstrate FlowSig's utility by applying it to various studies, showing that FlowSig can capture stimulation-induced changes to paracrine signaling in pancreatic islets, demonstrate shifts in intercellular flows due to increasing COVID-19 severity and reconstruct morphogen-driven activator-inhibitor patterns in mouse embryogenesis.

This article focuses on screening the major secreted proteins by the ischemia-challenged cardiac stromal fibroblasts (CF), the assessment of their expression status and functional role in the post-ischemic left ventricle (LV) and in the ischemia-challenged CF culture and to phenotype CF at single cell resolution based on the positivity of the identified mediators. The expression level of CRSP2, HSP27, IL-8, Cofilin-1, and HSP90 in the LV tissues following coronary artery bypass graft (CABG) and myocardial infarction (MI) and CF cells followed the screening profile derived from the MS/MS findings. The histology data unveiled ECM disorganization, inflammation and fibrosis reflecting the ischemic pathology. CRSP2, HSP27, and HSP90 were significantly upregulated in the LV-CABG tissues with a concomitant reduction ion LV-MI whereas Cofilin-1, IL8, Nrf2, and Troponin I were downregulated in LV-CABG and increased in LV-MI. Similar trends were exhibited by ischemic CF. Single cell transcriptomics revealed multiple sub-phenotypes of CF based on their respective upregulation of CRSP2, HSP27, IL-8, Cofilin-1, HSP90, Troponin I and Nrf2 unveiling pathological and pro-healing phenotypes. Further investigations regarding the underlying signaling mechanisms and validation of sub-populations would offer novel translational avenues for the management of cardiac diseases.

Despite recent work linking mixed phenotype acute leukemia (MPAL) to certain genetic lesions, specific driver mutations remain undefined for a significant proportion of patients and no genetic subtype is predictive of clinical outcomes. Moreover, therapeutic strategy for MPAL remains unclear, and prognosis is overall poor. We performed multiomic single cell profiling of 14 newly diagnosed adult MPAL patients to characterize the inter- and intra-tumoral transcriptional, immunophenotypic, and genetic landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. Despite this, we find that MPAL blasts express a shared stem cell-like transcriptional profile indicative of high differentiation potential. Patients with the highest differentiation potential demonstrate inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in the most stem-like MPAL cells, is applicable to bulk RNA sequencing data and is predictive of survival in an independent patient cohort, suggesting a potential strategy for clinical risk stratification.

The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e., staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene coexpression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data, streamlining tasks that are infeasible by conventional manual approaches.

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.

Understanding cell fate decision-making during complex biological processes is an open challenge that is now aided by high-resolution single-cell sequencing technologies. Specifically, it remains challenging to identify and characterize transition states corresponding to "tipping points" whereby cells commit to new cell states. Here, we present a computational method that takes advantage of single-cell transcriptomics data to infer the stability and gene regulatory networks (GRNs) along cell lineages. Our method uses the unspliced and spliced counts from single-cell RNA sequencing data and cell ordering along lineage trajectories to train an RNA splicing multivariate model, from which cell-state stability along the lineage is inferred based on spectral analysis of the model's Jacobian matrix. Moreover, the model infers the RNA cross-species interactions resulting in GRNs and their variation along the cell lineage. When applied to epithelial-mesenchymal transition in ovarian and lung cancer-derived cell lines, our model predicts a saddle-node transition between the epithelial and mesenchymal states passing through an unstable, intermediate cell state. Furthermore, we show that the underlying GRN controlling epithelial-mesenchymal transition rearranges during the transition, resulting in denser and less modular networks in the intermediate state. Overall, our method represents a flexible tool to study cell lineages with a combination of theory-driven modeling and single-cell transcriptomics data.

Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimers disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimers disease.

Multiple myeloma is a treatable, but currently incurable, hematological malignancy of plasma cells characterized by diverse and complex tumor genetics for which precision medicine approaches to treatment are lacking. The Multiple Myeloma Research Foundations Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study ( NCT01454297 ) is a longitudinal, observational clinical study of newly diagnosed patients with multiple myeloma (n = 1,143) where tumor samples are characterized using whole-genome sequencing, whole-exome sequencing and RNA sequencing at diagnosis and progression, and clinical data are collected every 3 months. Analyses of the baseline cohort identified genes that are the target of recurrent gain-of-function and loss-of-function events. Consensus clustering identified 8 and 12 unique copy number and expression subtypes of myeloma, respectively, identifying high-risk genetic subtypes and elucidating many of the molecular underpinnings of these unique biological groups. Analysis of serial samples showed that 25.5% of patients transition to a high-risk expression subtype at progression. We observed robust expression of immunotherapy targets in this subtype, suggesting a potential therapeutic option.

Drosophila suzukii lay eggs in soft-skinned, ripening fruits, making this insect a serious threat to berry production. Since its 2008 introduction into North America, growers have used insecticides, such as pyrethroids and spinosads, as the primary approach for D. suzukii management, resulting in development of insecticide resistance in this pest. This study sought to identify the molecular mechanisms conferring insecticide resistance in these populations. We sequenced the transcriptomes of two pyrethroid- and two spinosad-resistant isofemale lines. In both pyrethroid-resistant lines and one spinosad-resistant line, we identified overexpression of metabolic genes that are implicated in resistance in other insect pests. In the other spinosad-resistant line, we observed an overexpression of cuticular genes that have been linked to resistance. Our findings enabled the development of molecular diagnostics that we used to confirm persistence of insecticide resistance in California, U.S.A. To validate these findings, we leveraged D. melanogaster mutants with reduced expression of metabolic or cuticular genes that were found to be upregulated in resistant D. suzukii to demonstrate that these genes are involved in promoting resistance. This study is the first to characterize the molecular mechanisms of insecticide resistance in D. suzukii and provides insights into how current management practices can be optimized.

BACKGROUND: RNA sequencing (RNA-Seq) offers profound insights into the complex transcriptomes of diverse biological systems. However, standard differential expression analysis pipelines based on DESeq2 and edgeR encounter challenges when applied to the immediate early transcriptomes of Chlamydia spp., obligate intracellular bacteria. These challenges arise from their reliance on assumptions that do not hold in scenarios characterized by extensive transcriptomic activation and limited repression. RESULTS: Standard analyses using unique chlamydial RNA-Seq reads alone identify nearly 300 upregulated and about 300 downregulated genes, significantly deviating from actual RNA-Seq read trends. By incorporating both chlamydial and host reads or adjusting for total sequencing depth, the revised normalization methods each detected over 700 upregulated genes and 30 or fewer downregulated genes, closely aligned with observed RNA-Seq data. Further validation through qRT-PCR analysis confirmed the effectiveness of these adjusted approaches in capturing the true extent of transcriptomic activation during the immediate early phase of chlamydial infection. CONCLUSIONS: This study highlights the limitations of standard RNA-Seq analysis tools in scenarios with extensive transcriptomic activation, such as in Chlamydia spp. during early infection. Our revised normalization methods, incorporating host reads or total sequencing depth, provide a more accurate representation of gene expression dynamics. These approaches may inform similar adjustments in other systems with unbalanced gene expression dynamics, enhancing the accuracy of transcriptomic analysis.

Merging diverse single-cell RNA sequencing (scRNA-seq) data from numerous experiments, laboratories and technologies can uncover important biological insights. Nonetheless, integrating scRNA-seq data encounters special challenges when the datasets are composed of diverse cell type compositions. Scanorama offers a robust solution for improving the quality and interpretation of heterogeneous scRNA-seq data by effectively merging information from diverse sources. Scanorama is designed to address the technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of multiple scRNA-seq datasets. Here we provide a detailed protocol for using Scanorama within a Scanpy-based single-cell analysis workflow coupled with Google Colaboratory, a cloud-based free Jupyter notebook environment service. The protocol involves Scanorama integration, a process that typically spans 0.5-3 h. Scanorama integration requires a basic understanding of cellular biology, transcriptomic technologies and bioinformatics. Our protocol and new Scanorama-Colaboratory resource should make scRNA-seq integration more widely accessible to researchers.

Microgravity has been shown to lead to both muscle atrophy and impaired muscle regeneration. The purpose was to study the efficacy of microgravity to model impaired muscle regeneration in an engineered muscle platform and then to demonstrate the feasibility of performing drug screening in this model. Engineered human muscle was launched to the International Space Station National Laboratory, where the effect of microgravity exposure for 7 days was examined by transcriptomics and proteomics approaches. Gene set enrichment analysis of engineered muscle cultured in microgravity, compared to normal gravity conditions, highlighted a metabolic shift toward lipid and fatty acid metabolism, along with increased apoptotic gene expression. The addition of pro-regenerative drugs, insulin-like growth factor-1 (IGF-1) and a 15-hydroxyprostaglandin dehydrogenase inhibitor (15-PGDH-i), partially inhibited the effects of microgravity. In summary, microgravity mimics aspects of impaired myogenesis, and the addition of these drugs could partially inhibit the effects induced by microgravity.

BACKGROUND: Deeper insights into ERBB2-driven cancers are essential to develop new treatment approaches for ERBB2+ breast cancers (BCs). We employed the Collaborative Cross (CC) mouse model to unearth genetic factors underpinning Erbb2-driven mammary tumour development and metastasis. METHODS: 732 F1 hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains were monitored for mammary tumour phenotypes. GWAS pinpointed SNPs that influence various tumour phenotypes. Multivariate analyses and models were used to construct the polygenic score and to develop a mouse tumour susceptibility gene signature (mTSGS), where the corresponding human ortholog was identified and designated as hTSGS. The importance and clinical value of hTSGS in human BC was evaluated using public datasets, encompassing TCGA, METABRIC, GSE96058, and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validated in vivo using genetically diverse MMTV-Erbb2 mice. FINDINGS: Distinct variances in tumour onset, multiplicity, and metastatic patterns were observed in F1-hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified specific SNPs significantly associated with tumour onset, multiplicity, lung metastasis, and liver metastasis. Multivariate analyses flagged SNPs in 20 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b, Rer1, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a, Oxr1, Plxna2, and Tbc1d31) independently tied to various tumour characteristics, designated as a mTSGS. hTSGS scores (hTSGSS) based on their transcriptional level showed prognostic values, superseding clinical factors and PAM50 subtype across multiple human BC cohorts, and predicted pathological complete response independent of and superior to MammaPrint score in I-SPY2 study. The power of mTSGS score for predicting chemotherapy response was further validated in an in vivo mouse MMTV-Erbb2 model, showing that, like findings in human patients, mouse tumours with low mTSGS scores were most likely to respond to treatment. INTERPRETATION: Our investigation has unveiled many new genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that hTSGS holds promise as a biomarker for refining treatment strategies for patients with BC. FUNDING: The U.S. Department of Defense (DoD) Breast Cancer Research Program (BCRP) (BC190820), United States; MCIN/AEI/10.13039/501100011039 (PID2020-118527RB-I00, PDC2021-121735-I00), the European Union Next Generation EU/PRTR, the Regional Government of Castile and León (CSI144P20), European Union.

Natural killer (NK) cells are innate lymphoid cells (ILCs) contributing to immune responses to microbes and tumors. Historically, their classification hinged on a limited array of surface protein markers. Here, we used single-cell RNA sequencing (scRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to dissect the heterogeneity of NK cells. We identified three prominent NK cell subsets in healthy human blood: NK1, NK2 and NK3, further differentiated into six distinct subgroups. Our findings delineate the molecular characteristics, key transcription factors, biological functions, metabolic traits and cytokine responses of each subgroup. These data also suggest two separate ontogenetic origins for NK cells, leading to divergent transcriptional trajectories. Furthermore, we analyzed the distribution of NK cell subsets in the lung, tonsils and intraepithelial lymphocytes isolated from healthy individuals and in 22 tumor types. This standardized terminology aims at fostering clarity and consistency in future research, thereby improving cross-study comparisons.

ObjectivePeople with HIV (PWH) are at greater risk of developing lung diseases even when they are antiretroviral therapy (ART)-adherent and virally suppressed. The most common pulmonary function abnormality in PWH is that of impaired diffusing capacity of the lungs for carbon monoxide (DL CO ), which is an independent risk factor for increased mortality in PWH. Earlier work has identified several plasma biomarkers of inflammation and immune activation to be associated with decreased DL CO . However, the underpinning molecular mechanisms of HIV-associated impaired DL CO are largely unknown.DesignCross-sectional pilot study with PWH with normal DL CO (values greater than or equal to the lower limit of normal, DL CO  ≥ LLN, N = 9) or abnormal DL CO (DL CO

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p 0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

Melanoma clinical outcomes emerge from incompletely understood genetic mechanisms operating within the tumor and its microenvironment. Here, we used single-cell RNA-based spatial molecular imaging (RNA-SMI) in patient-derived archival tumors to reveal clinically relevant markers of malignancy progression and prognosis. We examined spatial gene expression of 203,472 cells inside benign and malignant melanocytic neoplasms, including melanocytic nevi and primary invasive and metastatic melanomas. Algorithmic cell clustering paired with intratumoral comparative two-dimensional analyses visualized synergistic, spatial gene signatures linking cellular proliferation, metabolism, and malignancy, validated by protein expression. Metastatic niches included up-regulation of CDK2 and FABP5, which independently predicted poor clinical outcome in 473 patients with melanoma via Cox regression analysis. More generally, our work demonstrates a framework for applying single-cell RNA-SMI technology toward identifying gene regulatory landscapes pertinent to cancer progression and patient survival.

Bone marrow aspirate concentrate (BMAC) and adipose-derived stromal vascular fraction (ADSVF) are the most marketed stem cell therapies to treat a variety of conditions in the general population and elite athletes. Both tissues have been used interchangeably clinically even though their detailed composition, heterogeneity, and mechanisms of action have neither been rigorously inventoried nor compared. This lack of information has prevented investigations into ideal dosages and has facilitated anecdata and misinformation. Here, we analyzed single-cell transcriptomes, proteomes, and flow cytometry profiles from paired clinical-grade BMAC and ADSVF. This comparative transcriptional atlas challenges the prevalent notion that there is one therapeutic cell type present in both tissues. We also provide data of surface markers that may enable isolation and investigation of cell (sub)populations. Furthermore, the proteome atlas highlights intertissue and interpatient heterogeneity of injected proteins with potentially regenerative or immunomodulatory capacities. An interactive webtool is available online.

Cardiac allograft vasculopathy (CAV) causes late graft failure and mortality after heart transplantation. Donor-specific antibodies (DSAs) lead to chronic endothelial cell injury, inflammation, and arterial intimal thickening. In this study, GeoMx digital spatial profiling was used to analyze arterial areas of interest (AOIs) from CAV+DSA+ rejected cardiac allografts (N = 3; 22 AOIs total). AOIs were categorized based on CAV neointimal thickening and underwent whole transcriptome and protein profiling. By comparing our transcriptomic data with that of healthy control vessels of rapid autopsy myocardial tissue, we pinpointed specific pathways and transcripts indicative of heightened inflammatory profiles in CAV lesions. Moreover, we identified protein and transcriptomic signatures distinguishing CAV lesions exhibiting low and high neointimal lesions. AOIs with low neointima showed increased markers for activated inflammatory infiltrates, endothelial cell activation transcripts, and gene modules involved in metalloproteinase activation and TP53 regulation of caspases. Inflammatory and apoptotic proteins correlated with inflammatory modules in low neointima AOIs. High neointima AOIs exhibited elevated TGFβ-regulated transcripts and modules enriched for platelet activation/aggregation. Proteins associated with growth factors/survival correlated with modules enriched for proliferation/repair in high neointima AOIs. Our findings reveal novel insight into immunological mechanisms mediating CAV pathogenesis.

Facioscapulohumeral muscular dystrophy (FSHD) is linked to abnormal derepression of the transcription activator DUX4. This effect is localized to a low percentage of cells, requiring single-cell analysis. However, single-cell/nucleus RNA-seq cannot fully capture the transcriptome of multinucleated large myotubes. To circumvent these issues, we use multiplexed error-robust fluorescent in situ hybridization (MERFISH) spatial transcriptomics that allows profiling of RNA transcripts at a subcellular resolution. We simultaneously examined spatial distributions of 140 genes, including 24 direct DUX4 targets, in in vitro differentiated myotubes and unfused mononuclear cells (MNCs) of control, isogenic D4Z4 contraction mutant and FSHD patient samples, as well as the individual nuclei within them. We find myocyte nuclei segregate into two clusters defined by the expression of DUX4 target genes, which is exclusively found in patient/mutant nuclei, whereas MNCs cluster based on developmental states. Patient/mutant myotubes are found in FSHD-hi and FSHD-lo states with the former signified by high DUX4 target expression and decreased muscle gene expression. Pseudotime analyses reveal a clear bifurcation of myoblast differentiation into control and FSHD-hi myotube branches, with variable numbers of DUX4 target-expressing nuclei found in multinucleated FSHD-hi myotubes. Gene coexpression modules related to extracellular matrix and stress gene ontologies are significantly altered in patient/mutant myotubes compared with the control. We also identify distinct subpathways within the DUX4 gene network that may differentially contribute to the disease transcriptomic phenotype. Taken together, our MERFISH-based study provides effective gene network profiling of multinucleated cells and identifies FSHD-induced transcriptomic alterations during myoblast differentiation.

During pulmonary mucormycosis, inhaled sporangiospores adhere to, germinate, and invade airway epithelial cells to establish infection. We provide evidence that HIF1α plays dual roles in airway epithelial cells during Mucorales infection. We observed an increase in HIF1α protein accumulation and increased expression of many known HIF1α-responsive genes during in vitro infection, indicating that HIF1α signaling is activated by Mucorales infection. Inhibition of HIF1α signaling led to a substantial decrease in the ability of R. delemar to invade cultured airway epithelial cells. Transcriptome analysis revealed that R. delemar infection induces the expression of many pro-inflammatory genes whose expression was significantly reduced by HIF1α inhibition. Importantly, pharmacological inhibition of HIF1α increased survival in a mouse model of pulmonary mucormycosis without reducing fungal burden. These results suggest that HIF1α plays two opposing roles during mucormycosis: one that facilitates the ability of Mucorales to invade the host cells and one that facilitates the ability of the host to mount an innate immune response.

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Meningiomas, although mostly benign, can be recurrent and fatal. World Health Organization (WHO) grading of the tumor does not always identify high-risk meningioma, and better characterizations of their aggressive biology are needed. To approach this problem, we combined 13 bulk RNA sequencing (RNA-seq) datasets to create a dimension-reduced reference landscape of 1,298 meningiomas. The clinical and genomic metadata effectively correlated with landscape regions, which led to the identification of meningioma subtypes with specific biological signatures. The time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape, where the nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcomes. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

MOTIVATION: Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions in their native microenvironments. Due to technology limitations, most spatial transcriptomic technologies still yield incomplete expression measurements with excessive missing values. Therefore, gene imputation is critical to filling in missing data, enhancing resolution, and improving overall interpretability. However, existing methods either require additional matched single-cell RNA-seq data, which is rarely available, or ignore spatial proximity or expression similarity information. RESULTS: To address these issues, we introduce Impeller, a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Impeller has two unique characteristics distinct from existing approaches. First, it builds a heterogeneous graph with two types of edges representing spatial proximity and expression similarity. Therefore, Impeller can simultaneously model smooth gene expression changes across spatial dimensions and capture similar gene expression signatures of faraway cells from the same type. Moreover, Impeller incorporates both short- and long-range cell-to-cell interactions (e.g. via paracrine and endocrine) by stacking multiple GNN layers. We use a learnable path operator in Impeller to avoid the over-smoothing issue of the traditional Laplacian matrices. Extensive experiments on diverse datasets from three popular platforms and two species demonstrate the superiority of Impeller over various state-of-the-art imputation methods. AVAILABILITY AND IMPLEMENTATION: The code and preprocessed data used in this study are available at https://github.com/aicb-ZhangLabs/Impeller and https://zenodo.org/records/11212604.

With aging skeletal muscle fibers undergo repeating cycles of denervation and reinnervation. In approximately the 8th decade of life reinnervation no longer keeps pace, resulting in the accumulation of persistently denervated muscle fibers that in turn cause an acceleration of muscle dysfunction. The significance of denervation in important clinical outcomes with aging is poorly studied. The Study of Muscle, Mobility, and Aging (SOMMA) is a large cohort study with the primary objective to assess how aging muscle biology impacts clinically important traits. Using transcriptomics data from vastus lateralis muscle biopsies in 575 participants we have selected 49 denervation-responsive genes to provide insights to the burden of denervation in SOMMA, to test the hypothesis that greater expression of denervation-responsive genes negatively associates with SOMMA participant traits that included time to walk 400 meters, fitness (VO2peak), maximal mitochondrial respiration, muscle mass and volume, and leg muscle strength and power. Consistent with our hypothesis, increased transcript levels of: a calciumdependent intercellular adhesion glycoprotein (CDH15), acetylcholine receptor subunits (CHRNA1, CHRND, CHRNE), a glycoprotein promoting reinnervation (NCAM1), a transcription factor regulating aspects of muscle organization (RUNX1), and a sodium channel (SCN5A) were each negatively associated with at least 3 of these traits. VO2peak and maximal respiration had the strongest negative associations with 15 and 19 denervation-responsive genes, respectively. In conclusion, the abundance of denervationresponsive gene transcripts is a significant determinant of muscle and mobility outcomes in aging humans, supporting the imperative to identify new treatment strategies to restore innervation in advanced age.

The inability to scalably and precisely measure the activity of developmental cis-regulatory elements (CREs) in multicellular systems is a bottleneck in genomics. Here we develop a dual RNA cassette that decouples the detection and quantification tasks inherent to multiplex single-cell reporter assays. The resulting measurement of reporter expression is accurate over multiple orders of magnitude, with a precision approaching the limit set by Poisson counting noise. Together with RNA barcode stabilization via circularization, these scalable single-cell quantitative expression reporters provide high-contrast readouts, analogous to classic in situ assays but entirely from sequencing. Screening >200 regions of accessible chromatin in a multicellular in vitro model of early mammalian development, we identify 13 (8 previously uncharacterized) autonomous and cell-type-specific developmental CREs. We further demonstrate that chimeric CRE pairs generate cognate two-cell-type activity profiles and assess gain- and loss-of-function multicellular expression phenotypes from CRE variants with perturbed transcription factor binding sites. Single-cell quantitative expression reporters can be applied in developmental and multicellular systems to quantitatively characterize native, perturbed and synthetic CREs at scale, with high sensitivity and at single-cell resolution.

A quarter of humanity is estimated to have been exposed to Mycobacterium tuberculosis (Mtb) with a 5-10% risk of developing tuberculosis (TB) disease. Variability in responses to Mtb infection could be due to host or pathogen heterogeneity. Here, we focused on host genetic variation in a Peruvian population and its associations with gene regulation in monocyte-derived macrophages and dendritic cells (DCs). We recruited former household contacts of TB patients who previously progressed to TB (cases, n = 63) or did not progress to TB (controls, n = 63). Transcriptomic profiling of monocyte-derived DCs and macrophages measured the impact of genetic variants on gene expression by identifying expression quantitative trait loci (eQTL). We identified 330 and 257 eQTL genes in DCs and macrophages (False Discovery Rate (FDR) < 0.05), respectively. Four genes in DCs showed interaction between eQTL variants and TB progression status. The top eQTL interaction for a protein-coding gene was with FAH, the gene encoding fumarylacetoacetate hydrolase, which mediates the last step in mammalian tyrosine catabolism. FAH expression was associated with genetic regulatory variation in cases but not controls. Using public transcriptomic and epigenomic data of Mtb-infected monocyte-derived dendritic cells, we found that Mtb infection results in FAH downregulation and DNA methylation changes in the locus. Overall, this study demonstrates effects of genetic variation on gene expression levels that are dependent on history of infectious disease and highlights a candidate pathogenic mechanism through pathogen-response genes. Furthermore, our results point to tyrosine metabolism and related candidate TB progression pathways for further investigation.

BackgroundSkeletal muscle dysfunction is a common extrapulmonary manifestation of chronic obstructive pulmonary disease (COPD). Alterations in skeletal muscle myosin heavy chain expression, with reduced type I and increased type II myosin heavy chain expression, are associated with COPD severity when studied in largely male cohorts. The objectives of this study were (1) to define an abnormal myofibre proportion phenotype in both males and females with COPD and (2) to identify transcripts and transcriptional networks associated with abnormal myofibre proportion in COPD.MethodsForty-six participants with COPD were assessed for body composition, strength, endurance and pulmonary function. Skeletal muscle biopsies from the vastus lateralis were assayed for fibre-type distribution and cross-sectional area via immunofluorescence microscopy and RNA-sequenced to generate transcriptome-wide gene expression data. Sex-stratified k-means clustering of type I and IIx/IIax fibre proportions was used to define abnormal myofibre proportion in participants with COPD and contrasted with previously defined criteria. Single transcripts and weighted co-expression network analysis modules were tested for correlation with the abnormal myofibre proportion phenotype.ResultsAbnormal myofibre proportion was defined in males with COPD (n = 29) as 22% type IIx/IIax fibres and in females with COPD (n = 17) as 12% type IIx/IIax fibres. Half of the participants with COPD were classified as having an abnormal myofibre proportion. Participants with COPD and an abnormal myofibre proportion had lower median handgrip strength (26.1 vs. 34.0 kg, P = 0.022), 6-min walk distance (300 vs. 353 m, P = 0.039) and forced expiratory volume in 1 s-to-forced vital capacity ratio (0.42 vs. 0.48, P = 0.041) compared with participants with COPD and normal myofibre proportions. Twenty-nine transcripts were associated with abnormal myofibre proportions in participants with COPD, with the upregulated NEB, TPM1 and TPM2 genes having the largest fold differences. Co-expression network analysis revealed that two transcript modules were significantly positively associated with the presence of abnormal myofibre proportions. One of these co-expression modules contained genes classically associated with muscle atrophy, as well as transcripts associated with both type I and type II myofibres, and was enriched for genetic loci associated with bone mineral density.ConclusionsOur findings indicate that there are significant transcriptional alterations associated with abnormal myofibre proportions in participants with COPD. Transcripts canonically associated with both type I and type IIa fibres were enriched in a co-expression network associated with abnormal myofibre proportion, suggesting altered transcriptional regulation across multiple fibre types.

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

We present the Manatee variational autoencoder model to predict transcription factor (TF) perturbation-induced transcriptomes. We demonstrate that the Manatee in silico perturbation analysis recapitulates target transcriptomic phenotypes in diverse cellular lineage transitions. We further propose the Manatee in silico screening analysis for prioritizing TF combinations targeting desired transcriptomic phenotypes.

The SUVmax is a measure of FDG uptake and is related with tumor aggressiveness in thyroid cancer, however, its association with molecular pathways is unclear. Here, we investigated the relationship between SUVmax and gene expression profiles in 80 papillary thyroid cancer (PTC) patients. We conducted an analysis of DEGs and enriched pathways in relation to SUVmax and tumor size. SUVmax showed a positive correlation with tumor size and correlated with glucose metabolic process. The genes that indicate thyroid differentiation, such as SLC5A5 and TPO, were negatively correlated with SUVmax. Unsupervised analysis revealed that SUVmax positively correlated with DNA replication(r = 0.29, p = 0.009), pyrimidine metabolism(r = 0.50, p

Heat stress exposure in intermittent heat waves and subsequent exposure during war theaters pose a clinical challenge that can lead to multi-organ dysfunction and long-term complications in the elderly. Using an aged mouse model and high-throughput sequencing, this study investigated the molecular dynamics of the liver-brain connection during heat stress exposure. Distinctive gene expression patterns induced by periodic heat stress emerged in both brain and liver tissues. An altered transcriptome profile showed heat stress-induced altered acute phase response pathways, causing neural, hepatic, and systemic inflammation and impaired synaptic plasticity. Results also demonstrated that proinflammatory molecules such as S100B, IL-17, IL-33, and neurological disease signaling pathways were upregulated, while protective pathways like aryl hydrocarbon receptor signaling were downregulated. In parallel, Rantes, IRF7, NOD1/2, TREM1, and hepatic injury signaling pathways were upregulated. Furthermore, current research identified Orosomucoid 2 (ORM2) in the liver as one of the mediators of the liver-brain axis due to heat exposure. In conclusion, the transcriptome profiling in elderly heat-stressed mice revealed a coordinated network of liver-brain axis pathways with increased hepatic ORM2 secretion, possibly due to gut inflammation and dysbiosis. The above secretion of ORM2 may impact the brain through a leaky blood-brain barrier, thus emphasizing intricate multi-organ crosstalk.

Endurance exercise training is known to reduce risk for a range of complex diseases. However, the molecular basis of this effect has been challenging to study and largely restricted to analyses of either few or easily biopsied tissues. Extensive transcriptome data collected across 15 tissues during exercise training in rats as part of the Molecular Transducers of Physical Activity Consortium has provided a unique opportunity to clarify how exercise can affect tissue-specific gene expression and further suggest how exercise adaptation may impact complex disease-associated genes. To build this map, we integrate this multi-tissue atlas of gene expression changes with gene-disease targets, genetic regulation of expression, and trait relationship data in humans. Consensus from multiple approaches prioritizes specific tissues and genes where endurance exercise impacts disease-relevant gene expression. Specifically, we identify a total of 5523 trait-tissue-gene triplets to serve as a valuable starting point for future investigations [Exercise; Transcription; Human Phenotypic Variation].

BACKGROUND: Both defects in mismatch repair (dMMR) and high microsatellite instability (MSI-H) have been recognised as crucial biomarkers that guide treatment strategies and disease management in colorectal cancer (CRC). As MMR and MSI tests are being widely conducted, an increasing number of MSI-H tumours have been identified in CRCs with mismatch repair proficiency (pMMR). The objective of this study was to assess the clinical features of patients with pMMR/MSI-H CRC and elucidate the underlying molecular mechanism in these cases. METHODS: From January 2015 to December 2018, 1684 cases of pMMR and 401 dMMR CRCs were enrolled. Of those patients, 93 pMMR/MSI-H were identified. The clinical phenotypes and prognosis were analysed. Frozen and paraffin-embedded tissue were available in 35 patients with pMMR/MSI-H, for which comprehensive genomic and transcriptomic analyses were performed. FINDINGS: In comparison to pMMR/MSS CRCs, pMMR/MSI-H CRCs exhibited significantly less tumour progression and better long-term prognosis. The pMMR/MSI-H cohorts displayed a higher presence of CD8+ T cells and NK cells when compared to the pMMR/MSS group. Mutational signature analysis revealed that nearly all samples exhibited deficiencies in MMR genes, and we also identified deleterious mutations in MSH3-K383fs. INTERPRETATION: This study revealed pMMR/MSI-H CRC as a distinct subgroup within CRC, which manifests diverse clinicopathological features and long-term prognostic outcomes. Distinct features in the tumour immune-microenvironment were observed in pMMR/MSI-H CRCs. Pathogenic deleterious mutations in MSH3-K383fs were frequently detected, suggesting another potential biomarker for identifying MSI-H. FUNDING: This work was supported by the Science and Technology Commission of Shanghai Municipality (20DZ1100101).

Phenotypic variation among species is a product of evolutionary changes to developmental programs1,2. However, how these changes generate novel morphological traits remains largely unclear. Here we studied the genomic and developmental basis of the mammalian gliding membrane, or patagium-an adaptative trait that has repeatedly evolved in different lineages, including in closely related marsupial species. Through comparative genomic analysis of 15 marsupial genomes, both from gliding and non-gliding species, we find that the Emx2 locus experienced lineage-specific patterns of accelerated cis-regulatory evolution in gliding species. By combining epigenomics, transcriptomics and in-pouch marsupial transgenics, we show that Emx2 is a critical upstream regulator of patagium development. Moreover, we identify different cis-regulatory elements that may be responsible for driving increased Emx2 expression levels in gliding species. Lastly, using mouse functional experiments, we find evidence that Emx2 expression patterns in gliders may have been modified from a pre-existing program found in all mammals. Together, our results suggest that patagia repeatedly originated through a process of convergent genomic evolution, whereby regulation of Emx2 was altered by distinct cis-regulatory elements in independently evolved species. Thus, different regulatory elements targeting the same key developmental gene may constitute an effective strategy by which natural selection has harnessed regulatory evolution in marsupial genomes to generate phenotypic novelty.

Soil fungi belonging to different functional guilds, such as saprotrophs, pathogens, and mycorrhizal symbionts, play key roles in forest ecosystems. To date, no study has compared the actual gene expression of these guilds in different forest soils. We used metatranscriptomics to study the competition for organic resources by these fungal groups in boreal, temperate, and Mediterranean forest soils. Using a dedicated mRNA annotation pipeline combined with the JGI MycoCosm database, we compared the transcripts of these three fungal guilds, targeting enzymes involved in C- and N mobilization from plant and microbial cell walls. Genes encoding enzymes involved in the degradation of plant cell walls were expressed at a higher level in saprotrophic fungi than in ectomycorrhizal and pathogenic fungi. However, ectomycorrhizal and saprotrophic fungi showed similarly high expression levels of genes encoding enzymes involved in fungal cell wall degradation. Transcripts for N-related transporters were more highly expressed in ectomycorrhizal fungi than in other groups. We showed that ectomycorrhizal and saprotrophic fungi compete for N in soil organic matter, suggesting that their interactions could decelerate C cycling. Metatranscriptomics provides a unique tool to test controversial ecological hypotheses and to better understand the underlying ecological processes involved in soil functioning and carbon stabilization.

Molecular profiling of clear cell renal cell carcinoma (ccRCC) tumors of patients in a clinical trial has identified distinct transcriptomic signatures with predictive value, yet data in non-clear cell variants (nccRCC) are lacking. We examined the transcriptional profiles of RCC tumors representing key molecular pathways, from a multi-institutional, real-world patient cohort, including ccRCC and centrally reviewed nccRCC samples. ccRCC had increased angiogenesis signature scores compared with the heterogeneous group of nccRCC tumors, while cell cycle, fatty acid oxidation/AMPK signaling, and fatty acid synthesis/pentose phosphate signature scores were increased in one or more nccRCC subtypes. Among both ccRCC and nccRCC tumors, T effector scores statistically correlated with increased immune cell infiltration and were more commonly associated with immunotherapy-related markers (PD-L1+/TMBhi/MSIhi). In conclusion, this study provides evidence of differential gene transcriptional profiles among ccRCC versus nccRCC tumors, providing insights for optimizing personalized and histology-specific therapeutic strategies for patients with advanced RCC.

BACKGROUND: Sulfur (S) is a mineral nutrient essential for plant growth and development, which is incorporated into diverse molecules fundamental for primary and secondary metabolism, plant defense, signaling, and maintaining cellular homeostasis. Although, S starvation response is well documented in the dicot model Arabidopsis thaliana, it is not clear if the same transcriptional networks control the response also in the monocots. RESULTS: We performed series of physiological, expression, and metabolite analyses in two model monocot species, one representing the C3 plants, Oryza sativa cv. kitaake, and second representing the C4 plants, Setaria viridis. Our comprehensive transcriptomic analysis revealed twice as many differentially expressed genes (DEGs) in S. viridis than in O. sativa under S-deficiency, consistent with a greater loss of sulfur and S-containing metabolites under these conditions. Surprisingly, most of the DEGs and enriched gene ontology terms were species-specific, with an intersect of only 58 common DEGs. The transcriptional networks were different in roots and shoots of both species, in particular no genes were down-regulated by S-deficiency in the roots of both species. CONCLUSIONS: Our analysis shows that S-deficiency seems to have different physiological consequences in the two monocot species and their nutrient homeostasis might be under distinct control mechanisms.

Dynamically organized chromatin complexes often involve multiplex chromatin interactions and sometimes chromatin-associated RNA1-3. Chromatin complex compositions change during cellular differentiation and ageing, and are expected to be highly heterogeneous among terminally differentiated single cells4-7. Here we introduce the multinucleic acid interaction mapping in single cells (MUSIC) technique for concurrent profiling of multiplex chromatin interactions, gene expression and RNA-chromatin associations within individual nuclei. When applied to 14 human frontal cortex samples from older donors, MUSIC delineated diverse cortical cell types and states. We observed that nuclei exhibiting fewer short-range chromatin interactions were correlated with both an older transcriptomic signature and Alzheimers disease pathology. Furthermore, the cell type exhibiting chromatin contacts between cis expression quantitative trait loci and a promoter tends to be that in which these cis expression quantitative trait loci specifically affect the expression of their target gene. In addition, female cortical cells exhibit highly heterogeneous interactions between XIST non-coding RNA and chromosome X, along with diverse spatial organizations of the X chromosomes. MUSIC presents a potent tool for exploration of chromatin architecture and transcription at cellular resolution in complex tissues.

Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.

In situ transcriptomic techniques promise a holistic view of tissue organization and cell-cell interactions. There has been a surge of multiplexed RNA in situ mapping techniques but their application to human tissues has been limited due to their large size, general lower tissue quality and high autofluorescence. Here we report DART-FISH, a padlock probe-based technology capable of profiling hundreds to thousands of genes in centimeter-sized human tissue sections. We introduce an omni-cell type cytoplasmic stain that substantially improves the segmentation of cell bodies. Our enzyme-free isothermal decoding procedure allows us to image 121 genes in large sections from the human neocortex in 20 healthy and pathological cell states, and identified diseased niches enriched in transcriptionally altered epithelial cells and myofibroblasts.