Learning Enhancer-Gene associations from Bulk Transcriptomic and Epigenetic Sequencing Data with STITCHIT.

To reveal gene regulation mechanisms, it is essential to understand the role of regulatory elements, which are possibly distant from gene promoters. Integrative analysis of epigenetic and transcriptomic data can be used to gain insights into gene-expression regulation in specific phenotypes. Here, we discuss STITCHIT, an approach to dissect epigenetic variation in a gene-specific manner across many samples for the identification of regulatory elements without relying on peak calling algorithms. The obtained genomic regions are then further refined using a regularized linear model approach, which can also be used to predict gene expression. We illustrate the use of STITCHIT using H3k27ac ChIP-seq and RNA-seq data from the International Human Epigenome Consortium (IHEC).

Transcriptomics in the Study of Antiviral Innate Immunity.

Transcriptomics is an extremely important area of molecular biology and is a powerful tool for studying all RNA molecules in an organism. Conventional transcriptomic technologies include microarrays and RNA sequencing, and the rapid development of single-cell sequencing and spatial transcriptomics in recent years has provided an enormous scope for research in this field. This chapter describes the application, significance, and experimental procedures of a variety of transcriptomic technologies in antiviral natural immunity.

Lens Regeneration: The Application of iSyTE and In Silico Approaches to Evaluate Gene Expression in Lens Organoids.

Several protocols have been established for the generation of lens organoids from embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and other cells with regenerative potential in humans or various animal models. It is important to examine how well the regenerated lens organoids reflect lens biology, in terms of its development, homeostasis, and aging. Toward this goal, the iSyTE database (integrated Systems Tool for Eye gene discovery; https://research.bioinformatics.udel.edu/iSyTE/ ), a bioinformatics resource tool that contains meta-analyzed gene expression data in wild-type lens across different embryonic, postnatal, and adult stages, can serve as a resource for comparative analysis. This article outlines the approaches toward effective use of iSyTE to gain insights into normal gene expression in the mouse lens, enriched expression in the lens, and differential gene expression in select mouse gene-perturbation cataract/lens defects models, which in turn can be used to evaluate expression of key lens-relevant genes in lens organoids by transcriptomics (e.g., RNA-sequencing (RNA-seq), microarrays, etc.) or other downstream methods (e.g., RT-qPCR, etc.).

Transcriptome wide analysis of MADS box genes in Crocus sativus and interplay of CstMADS19-CstMADS26 in orchestrating apocarotenoid biosynthesis.

Flowers of Crocus sativus L. are immensely important not only for arrangement of floral whorls but more because each floral organ is dominated by a different class of specialized compounds. Dried stigmas of C. sativus flowers form commercial saffron, and are known to accumulate unique apocarotenoids like crocin, picrocrocin and safranal. Inspite of being a high value crop, the molecular mechanism regulating flower development in Crocus remains largely unknown. Moreover, it would be very interesting to explore any co-regulatory mechanism which controls floral architecture and secondary metabolic pathways which exist in specific floral organs. Here we report transcriptome wide identification of MADS box genes in Crocus. A total of 39 full length MADS box genes were identified among which three belonged to type I and 36 to type II class. Phylogeny classified them into 11 sub-clusters. Expression pattern revealed some stigma up-regulated genes among which CstMADS19 encoding an AGAMOUS gene showed high expression. Transient over-expression of CstMADS19 in stigmas of Crocus resulted in increased crocin by enhancing expression of pathway genes. Yeast one hybrid assay demonstrated that CstMADS19 binds to promoters of phytoene synthase and carotenoid cleavage dioxygenase 2 genes. Yeast two hybrid and BiFC assays confirmed interaction of CstMADS19 with CstMADS26 which codes for a SEPALATA gene. Co-overexpression of CstMADS19 and CstMADS26 in Crocus stigmas enhanced crocin content more than was observed when genes were expressed individually. Collectively, these findings indicate that CstMADS19 functions as a positive regulator of stigma based apocarotenoid biosynthesis in Crocus.

Study on the underlying mechanism of Huachansu Capsule induced cardiotoxicity of normal rat by integrating transcriptomics, metabolomics and network toxicology.

ETHNOPHARMACOLOGICAL RELEVANCE: Huachansu Capsule (HCSc) is a simple enteric-coated capsule refined from the skin of the dried toad, a traditional medicinal herb. It has been used clinically for many years to treat a variety of malignant tumors with remarkable efficacy. To date, a number of main components of HCSc have been reported to be cardiotoxic, but the specific mechanism of cardiotoxicity is still unknown. AIM OF THE STUDY: The aim of this study was to elucidate the possible cardiotoxic symptoms caused by high-doses of HCSc and to further reveal the complex mechanisms by which it causes cardiotoxicity. MATERIALS AND METHODS: UPLC-Q-Exactive Orbitrap MS and network toxicology were used to identify and predict the potential toxic components, related signaling pathways. Then, we used acute and sub-acute toxicity experiments to reveal the apparent phenomenon of HCSc-induced cardiotoxicity. Finally, we combined transcriptomics and metabolomics to elucidate the potential mechanism of action, and verified the putative mechanism by molecular docking, RT-qPCR, and Western blot. RESULTS: We found 8 toad bufadienolides components may be induced cardiac toxicity HCSc main toxic components. Through toxicity experiments, we found that high dose of HCSc could increase a variety of blood routine indexes, five cardiac enzymes, heart failure indexes (BNP), troponin (cTnI and cTnT), heart rate and the degree of heart tissue damage, while low-dose of HCSc had no such changes. In addition, by molecular docking, found that 8 kinds of main toxic components and cAMP, AMPK, IL1ß, mTOR all can be a very good combination, especially in the cAMP. Meanwhile, RT-qPCR and Western blot results showed that HCSc could induce cardiotoxicity by regulating a variety of heart-related differential genes and activating the cAMP signaling pathway. CONCLUSIONS: In this study, network toxicology, transcriptomics and metabolomics were used to elucidate the complex mechanism of possible cardiotoxicity induced by high-dose HCSc. Animal experiments, molecular docking, Western blot and RT-qPCR experiments were also used to verify the above mechanism. These findings will inform further mechanistic studies and provide theoretical support for its safe clinical application.

Inhibition mechanisms of perchlorate on the photosynthesis of cyanobacterium Synechocystis sp. PCC6803: Insights from physiology and transcriptome analysis.

Perchlorate (ClO4-) is a type of novel persistent inorganic pollutant that has gained increasing attention because of its high solubility, poor degradability, and widespread distribution. However, the impacts of perchlorate on aquatic autotrophs such cyanobacterium are still unclear. Herein, Synechocystis sp. PCC6803 (Synechocystis) was used to investigate the response mechanisms of perchlorate on cyanobacterium by integrating physiological and transcriptome analyses. Physiological results showed that perchlorate mainly damaged the photosystem of Synechocystis, and the inhibition degree of photosystem II (PSII) was severer than that of photosystem I (PSI). When the exposed cells were moved to a clean medium, the photosynthetic activities were slightly repaired but still lower than in the control group, indicating irreversible damage. Furthermore, perchlorate also destroyed the cellular ultrastructure and induced oxidative stress in Synechocystis. The antioxidant glutathione (GSH) content and the superoxide dismutase (SOD) enzyme activity were enhanced to scavenge harmful reactive oxygen (ROS) in Synechocystis. Transcriptome analysis revealed that the genes associated with "photosynthesis" and "electron transport" were significantly regulated. For instance, most genes related to PSI (e.g., psaf, psaJ) and the "electron transport chain" were upregulated, whereas most genes related to PSII (e.g., psbA3, psbD1, psbB, and psbC) were downregulated. Additionally, perchlorate also induced the expression of genes related to the antioxidant system (sod2, gpx, gst, katG, and gshB) to reduce oxidative damage. Overall, this study is the first to investigate the impacts and mechanisms of cyanobacterium under perchlorate stress, which is conducive to assessing the risk of perchlorate in aquatic environments.

Transcriptomic profiling of human mesenchymal stem cells using a pulsed electromagnetic-wave motion bioreactor system for enhanced osteogenic commitment and therapeutic potentials.

Traditional bioreactor systems involve the use of three-dimensional (3D) scaffolds or stem cell aggregates, limiting the accessibility to the production of cell-secreted biomolecules. Herein, we present the use a pulse electromagnetic fields (pEMFs)-assisted wave-motion bioreactor system for the dynamic and scalable culture of human bone marrow-derived mesenchymal stem cells (hBMSCs) with enhanced the secretion of various soluble factors with massive therapeutic potential. The present study investigated the influence of dynamic pEMF (D-pEMF) on the kinetic of hBMSCs. A 30-min exposure of pEMF (10V-1Hz, 5.82 G) with 35 oscillations per minute (OPM) rocking speed can induce the proliferation (1 × 105 â†’ 4.5 × 105) of hBMSCs than static culture. Furthermore, the culture of hBMSCs in osteo-induction media revealed a greater enhancement of osteogenic transcription factors under the D-pEMF condition, suggesting that D-pEMF addition significantly boosted hBMSCs osteogenesis. Additionally, the RNA sequencing data revealed a significant shift in various osteogenic and signaling genes in the D-pEMF group, further suggesting their osteogenic capabilities. In this research, we demonstrated that the combined effect of wave and pEMF stimulation on hBMSCs allows rapid proliferation and induces osteogenic properties in the cells. Moreover, our study revealed that D-pEMF stimuli also induce ROS-scavenging properties in the cultured cells. This study also revealed a bioactive and cost-effective approach that enables the use of cells without using any expensive materials and avoids the possible risks associated with them post-implantation.

Elucidating the downstream pathways triggered by H2S signaling in Arabidopsis thaliana under drought stress via transcriptome analysis.

Hydrogen sulfide (H2S) is a crucial signaling molecule in plants. Recent studies have shown that H2S plays an equally important role as nitric oxide (NO) and hydrogen peroxide (H2O2) in plant signaling. Previous studies have demonstrated the involvement of H2S in regulating drought and other stressful environmental conditions, but the exact downstream molecular mechanisms activated by the H2S signaling molecule remain unclear. In this study, we conducted a comprehensive genome-wide transcriptomic analysis of both wild type (WT) and double mutant (lcd/des1). Arabidopsis thaliana plants were exposed to 40% polyethylene glycol (PEG) to induce drought stress and 20 µM sodium hydrosulfide (NaHS). The resulting transcriptome data were analyzed for differentially significant genes and their statistical enrichments in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The results indicated significant upregulation of genes related to photosynthesis, carbon fixation, plant secondary metabolite biosynthesis, inositol and phosphatidylinositol signaling pathways, and stress-responsive pathways in mutant plants under drought stress. Mutant plants with impaired H2S signaling mechanisms displayed greater susceptibility to drought stress compared to wild-type plants. In summary, all findings highlight the pivotal role of H2S signaling in stimulating other drought-responsive signaling pathways.

Transcriptome dataset of mouse adipose tissue across estrous cycles.

Adipose tissue is crucial for energy storage and release, ensuring energy homeostasis within the body. Disturbances in the physiology of adipose tissue have been associated with various health disorders, such as obesity and diabetes. The reproductive cycle represents a fundamental biological pattern in female physiology. Although previous research has highlighted the substantial regulatory influence of ovarian hormones on adipose tissue, our understanding of the comprehensive changes in adipose tissue throughout the reproductive cycle remains limited. In this study, we examined the transcriptomic profile of female mouse-adipose tissue across their complete estrous cycles. The findings provided detailed descriptions of the datasets generated, including information on data collection, processing, and quality control. The study also demonstrated the robustness of these data through various validation steps. These findings serve as crucial resources for investigating the role of estrous cycle rhythmicity in important adipose tissue processes in the future.

Differences in transcriptomic responses upon Phytophthora palmivora infection among cultivars reveal potential underlying resistant mechanisms in durian.

BACKGROUND: Phytophthora palmivora is a devastating oomycete pathogen in durian, one of the most economically important crops in Southeast Asia. The use of fungicides in Phytophthora management may not be a long-term solution because of emerging chemical resistance issues. It is crucial to develop Phytophthora-resistant durian cultivars, and information regarding the underlying resistance mechanisms is valuable for smart breeding programs. RESULTS: In this study, we conducted RNA sequencing (RNA-seq) to investigate early gene expression responses (at 8, 24, and 48 h) after the P. palmivora infection in three durian cultivars, which included one resistant cultivar (Puangmanee; PM) and two susceptible cultivars (Monthong; MT and Kradumthong; KD). We performed co-expression and differential gene expression analyses to capture gene expression patterns and identify the differentially expressed genes. The results showed that genes encoding heat shock proteins (HSPs) were upregulated in all infected durians. The expression levels of genes encoding HSPs, such as ERdj3B, were high only in infected PM. A higher level of P. palmivora resistance in PM appeared to be associated with higher expression levels of various genes encoding defense and chitin response proteins, such as lysM domain receptor-like kinases. MT had a lower resistance level than PM, although it possessed more upregulated genes during P. palmivora infection. Many photosynthetic and defense genes were upregulated in the infected MT, although their expression levels were lower than those in the infected PM. KD, the least resistant cultivar, showed downregulation of genes involved in cell wall organization or biogenesis during P. palmivora infection. CONCLUSIONS: Our results showed that the three durian cultivars exhibited significantly different gene expression patterns in response to P. palmivora infection. The upregulation of genes encoding HSPs was common in all studied durians. The high expression of genes encoding chitin response proteins likely contributed to P. palmivora resistance in durians. Durian susceptibility was associated with low basal expression of defense genes and downregulation of several cell wall-related genes. These findings enhance our understanding of durian resistance to Phytophthora infection and could be useful for the development of elite durian cultivars.

Establishment and characterization of 18 Sarcoma Cell Lines: Unraveling the Molecular Mechanisms of Doxorubicin Resistance in Sarcoma Cell Lines.

Sarcomas, malignant tumors from mesenchymal tissues, exhibit poor prognosis despite advancements in treatment modalities such as surgery, radiotherapy, and chemotherapy, with doxorubicin being a cornerstone treatment. Resistance to doxorubicin remains a significant hurdle in therapy optimization. This study aims to dissect the molecular bases of doxorubicin resistance in sarcoma cell lines, which could guide the development of tailored therapeutic strategies. Eighteen sarcoma cell lines from 14 patients were established under ethical approvals and classified into seven subtypes. Molecular, genomic, and transcriptomic analyses included whole-exome sequencing, RNA sequencing, drug sensitivity assays, and pathway enrichment studies to elucidate the resistance mechanisms. Variability in doxorubicin sensitivity was linked to specific genetic alterations, including mutations in TP53 and variations in the copy number of genomic loci like 11q24.2. Transcriptomic profiling divided cell lines into clusters by karyotype complexity, influencing drug responses. Additionally, pathway analyses highlighted the role of signaling pathways like WNT/BETA-CATENIN and HEDGEHOG in doxorubicin-resistant lines. Comprehensive molecular profiling of sarcoma cell lines has revealed complex interplays of genetic and transcriptomic factors dictating doxorubicin resistance, underscoring the need for personalized medicine approaches in sarcoma treatment. Further investigations into these resistance mechanisms could facilitate the development of more effective, customized therapy regimens.

Population structure analysis of Phlebotomus papatasi populations using transcriptome microsatellites: possible implications for leishmaniasis control and vaccine development.

BACKGROUND: Phlebotomus papatasi is considered the primary vector of Leishmania major parasites that cause zoonotic cutaneous leishmaniasis (ZCL) in the Middle East and North Africa. Phlebotomus papatasi populations have been studied extensively, revealing the existence of different genetic populations and subpopulations over its large distribution range. Genetic diversity and population structure analysis using transcriptome microsatellite markers is important to uncover the vector distribution dynamics, essential for controlling ZCL in endemic areas. METHODS: In this study, we investigated the level of genetic variation using expressed sequence tag-derived simple sequence repeats (EST-SSRs) among field and colony P. papatasi samples collected from 25 different locations in 11 countries. A total of 302 P. papatasi sand fly individuals were analyzed, including at least 10 flies from each region. RESULTS: The analysis revealed a high-level population structure expressed by five distinct populations A through E, with moderate genetic differentiation among all populations. These genetic differences in expressed genes may enable P. papatasi to adapt to different environmental conditions along its distribution range and likely affect dispersal. CONCLUSIONS: Elucidating the population structuring of P. papatasi is essential to L. major containment efforts in endemic countries. Moreover, the level of genetic variation among these populations may improve our understanding of Leishmania-sand fly interactions and contribute to the efforts of vaccine development based on P. papatasi salivary proteins.

Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones.

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.

Leveraging transcriptome sequence read archives for virus detection in wild and colony populations of triatomines (Hemiptera: Reduviidae: Triatominae).

Triatomines are infamous as vectors of the parasite Trypanosoma cruzi, the causative agent of Chagas disease. However, climate-driven range expansion and urbanization adaptation of triatomine populations, coupled with their highly diverse feeding strategies (vertebrate haematophagy, kleptohaematophagy, and coprophagy), and has elevated interest in triatomines as potential arboviral vectors. Information on the triatomine virome is scant, with prior records including only eight insect-specific viruses: Triatoma virus (TrV) and Rhodnius prolixus viruses 1-7. Here, we leverage publicly available transcriptome datasets to assess viral diversity in 122 wild and colony kissing bugs representing eight species from six countries. In total, six viruses were detected (including Rhodnius prolixus viruses 4-6), and TrV was detected in almost half of all screened triatomines. This is the first report of TrV in Triatoma brasiliensis and in members of the genus Mepraia (M. gajardoi, M. spinolai, and M. parapatrica), and this effort has vastly expanded the publicly available genomic resources of TrV, adding 39 genome sequences to the single genome sequence currently available in the GenBank database. Furthermore, two additional viruses-Meccus longipennis virus 1 and Drosophila melanogaster Nora virus-are herein reported for the first time from kissing bugs. Meccus longipennis virus 1 was detected in Triatoma infestans from Argentina, Brazil, Chile, and Peru, and Drosophila melanogaster Nora virus was found in T. infestans from Argentina. Our results illustrate the advantage and utility of low-cost transcriptome data mining for the discovery of known and novel arboviruses in triatomines and other potential insect vectors.

Characterizing the allele-specific gene expression landscape in high hyperdiploid acute lymphoblastic leukemia with BASE.

Somatic copy number variations (CNVs), including abnormal chromosome numbers and structural changes leading to gain or loss of genetic material, play a crucial role in initiation and progression of cancer. CNVs are believed to cause gene dosage imbalances and modify cis-regulatory elements, leading to allelic expression imbalances in genes that influence cell division and thereby contribute to cancer development. However, the impact of CNVs on allelic gene expression in cancer remains unclear. Allele-specific expression (ASE) analysis, a potent method for investigating genome-wide allelic imbalance profiles in tumors, assesses the relative expression of two alleles using high-throughput sequencing data. However, many existing methods for gene-level ASE detection rely on only RNA sequencing data, which present challenges in interpreting the genetic mechanisms underlying ASE in cancer. To address this issue, we developed a robust framework that integrates allele-specific copy number calls into ASE calling algorithms by leveraging paired genome and transcriptome data from the same sample. This integration enhances the interpretability of the genetic mechanisms driving ASE, thereby facilitating the identification of driver events triggered by CNVs in cancer. In this study, we utilized BASE to conduct a comprehensive analysis of ASE in high hyperdiploid acute lymphoblastic leukemia (HeH ALL), a prevalent childhood malignancy characterized by gains of chromosomes X, 4, 6, 10, 14, 17, 18, and 21. Our analysis unveiled the comprehensive ASE landscape in HeH ALL. Through a multi-perspective examination of HeH ASEs, we offer a systematic understanding of how CNVs impact ASE in HeH, providing valuable insights to guide ASE studies in cancer.

Imputing spatial transcriptomics through gene network constructed from protein language model.

Image-based spatial transcriptomic sequencing technologies have enabled the measurement of gene expression at single-cell resolution, but with a limited number of genes. Current computational approaches attempt to overcome these limitations by imputing missing genes, but face challenges regarding prediction accuracy and identification of cell populations due to the neglect of gene-gene relationships. In this context, we present stImpute, a method to impute spatial transcriptomics according to reference scRNA-seq data based on the gene network constructed from the protein language model ESM-2. Specifically, stImpute employs an autoencoder to create gene expression embeddings for both spatial transcriptomics and scRNA-seq data, which are used to identify the nearest neighboring cells between scRNA-seq and spatial transcriptomics datasets. According to the neighbored cells, the gene expressions of spatial transcriptomics cells are imputed through a graph neural network, where nodes are genes, and edges are based on cosine similarity between the ESM-2 embeddings of the gene-encoding proteins. The gene prediction uncertainty is further measured through a deep learning model. stImpute was shown to consistently outperform state-of-the-art methods across multiple datasets concerning imputation and clustering. stImpute also demonstrates robustness in producing consistent results that are insensitive to model parameters.

Human-augmented large language model-driven selection of glutathione peroxidase 4 as a candidate blood transcriptional biomarker for circulating erythroid cells.

The identification of optimal candidate genes from large-scale blood transcriptomic data is crucial for developing targeted assays to monitor immune responses. Here, we introduce a novel, optimized large language model (LLM)-based approach for prioritizing candidate biomarkers from blood transcriptional modules. Focusing on module M14.51 from the BloodGen3 repertoire, we implemented a multi-step LLM-driven workflow. Initial high-throughput screening used GPT-4, Claude 3, and Claude 3.5 Sonnet to score and rank the module's constituent genes across six criteria. Top candidates then underwent high-resolution scoring using Consensus GPT, with concurrent manual fact-checking and, when needed, iterative refinement of the scores based on user feedback. Qualitative assessment of literature-based narratives and analysis of reference transcriptome data further refined the selection process. This novel multi-tiered approach consistently identified Glutathione Peroxidase 4 (GPX4) as the top candidate gene for module M14.51. GPX4's role in oxidative stress regulation, its potential as a future drug target, and its expression pattern across diverse cell types supported its selection. The incorporation of reference transcriptome data further validated GPX4 as the most suitable candidate for this module. This study presents an advanced LLM-driven workflow with a novel optimized scoring strategy for candidate gene prioritization, incorporating human-in-the-loop augmentation. The approach identified GPX4 as a key gene in the erythroid cell-associated module M14.51, suggesting its potential utility for biomarker discovery and targeted assay development. By combining AI-driven literature analysis with iterative human expert validation, this method leverages the strengths of both artificial and human intelligence, potentially contributing to the development of biologically relevant and clinically informative targeted assays. Further validation studies are needed to confirm the broader applicability of this human-augmented AI approach.

Spatial multi-omics in whole skeletal muscle reveals complex tissue architecture.

Myofibers are large multinucleated cells that have long thought to have a rather simple organization. Single-nucleus transcriptomics, spatial transcriptomics and spatial metabolomics analysis have revealed distinct transcription profiles in myonuclei related to myofiber type. However, the use of local tissue collection or dissociation methods have obscured the spatial organization. To elucidate the full tissue architecture, we combine two spatial omics, RNA tomography and mass spectrometry imaging. This enables us to map the spatial transcriptomic, metabolomic and lipidomic organization of the whole murine tibialis anterior muscle. Our findings on heterogeneity in fiber type proportions are validated with multiplexed immunofluorescence staining in tibialis anterior, extensor digitorum longus and soleus. Our results demonstrate unexpectedly strong regionalization of gene expression, metabolic differences and variable myofiber type proportion along the proximal-distal axis. These new insights in whole-tissue level organization reconcile sometimes conflicting results coming from previous studies relying on local sampling methods.

Comparative transcriptome of normal and cancer-associated fibroblasts.

BACKGROUND: The characteristics of a tumor are largely determined by its interaction with the surrounding micro-environment (TME). TME consists of both cellular and non-cellular components. Cancer-associated fibroblasts (CAFs) are a major component of the TME. They are a source of many secreted factors that influence the survival and progression of tumors as well as their response to drugs. Identification of markers either overexpressed in CAFs or unique to CAFs would pave the way for novel therapeutic strategies that in combination with conventional chemotherapy are likely to have better patient outcome. METHODS: Fibroblasts have been derived from Benign Prostatic Hyperplasia (BPH) and prostate cancer. RNA from these has been used to perform a transcriptome analysis in order to get a comparative profile of normal and cancer-associated fibroblasts. RESULTS: The study has identified 818 differentially expressed mRNAs and 17 lincRNAs between normal and cancer-associated fibroblasts. Also, 15 potential lincRNA-miRNA-mRNA combinations have been identified which may be potential biomarkers. CONCLUSIONS: This study identified differentially expressed markers between normal and cancer-associated fibroblasts that would help in targeted therapy against CAFs/derived factors, in combination with conventional therapy. However, this would in future need more experimental validation.

Selective vulnerability of parvocellular oxytocin neurons in social dysfunction.

Selective vulnerability offers a conceptual framework for understanding neurodegenerative disorders such as Parkinson's disease, where specific neuronal types are selectively affected and adjacent ones are spared. However, the applicability of this framework to neurodevelopmental disorders, particularly those characterized by atypical social behaviors, such as autism spectrum disorder, remains uncertain. Here we show that an embryonic disturbance, known to induce social dysfunction in male mice, preferentially impaired the gene expression crucial for neural functions in parvocellular oxytocin (OT) neurons-a subtype linked to social rewards-while neighboring cell types experienced a lesser impact. Chemogenetic stimulation of OT neurons at the neonatal stage ameliorated social deficits in early adulthood, concurrent with cell-type-specific sustained recovery of pivotal gene expression within parvocellular OT neurons. Collectively, our data shed light on the transcriptomic selective vulnerability within the hypothalamic social behavioral center and provide a potential therapeutic target through specific neonatal neurostimulation.