Constituent Genes Research Articles

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.

TGF-β and RAS jointly unmask primed enhancers to drive metastasis

Epithelial-to-mesenchymal transitions (EMTs) and extracellular matrix (ECM) remodeling are distinct yet important processes during carcinoma invasion and metastasis. Transforming growth factor β (TGF-β) and RAS, signaling through SMAD and RAS-responsive element-binding protein 1 (RREB1), jointly trigger expression of EMT and fibrogenic factors as two discrete arms of a common transcriptional response in carcinoma cells. Here, we demonstrate that both arms come together to form a program for lung adenocarcinoma metastasis and identify chromatin determinants tying the expression of the constituent genes to TGF-β and RAS inputs. RREB1 localizes to H4K16acK20ac marks in histone H2A.Z-loaded nucleosomes at enhancers in the fibrogenic genes interleukin-11 (IL11), platelet-derived growth factor-B (PDGFB), and hyaluronan synthase 2 (HAS2), as well as the EMT transcription factor SNAI1, priming these enhancers for activation by a SMAD4-INO80 nucleosome remodeling complex in response to TGF-β. These regulatory properties segregate the fibrogenic EMT program from RAS-independent TGF-β gene responses and illuminate the operation and vulnerabilities of a bifunctional program that promotes metastatic outgrowth.

Differential microRNA expression in adolescent anxiety proneness.

Biological mechanisms underlying anxiety proneness (AP), the tendency to react fearfully to stressors due to the belief that experiencing anxiety has harmful consequences, remain unclear. Epigenetic mechanisms, such as microRNAs (small, non-coding RNAs 19-20 nucleotides long), may be contributory. This study investigated AP-associated differences in microRNA expression among South African adolescents with variable exposure to childhood trauma (CT). AP was assessed using a composite score reflecting trait anxiety and anxiety sensitivity, while CT exposure was assessed with the Childhood Trauma Questionnaire. High-quality total RNA (n = 88) extracted from whole blood underwent microRNA-sequencing. Differential microRNA expression analysis was conducted with DESeq2 in R, messenger RNA target prediction analysis was performed using TargetScan and DIANA-microT, and the DIANA mirPATH tool was used for KEGG pathway analysis. The majority of participants were female (75.86%) with an average age of 15 (±1.19) years. MicroRNA expression analysis identified upregulation of hsa-miR-28-5p and downregulation of hsa-miR-502-3p and hsa-miR-500a-3p in high-AP individuals, irrespective of CT. Four KEGG pathways, each with ≥10% of their constituent genes predicted to be targets of the differentially expressed microRNAs, were identified and were enriched for genes involved in calcineurin and glutamate signalling. These findings suggest that epigenetically mediated effects on neuronal function contribute to the molecular aetiology of AP.

A tumour-associated macrophage-based signature for deciphering prognosis and immunotherapy response in prostate cancer.

For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour-associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy-sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC-XENA, GEO and TISCH databases were extracted for analysis. Based on both single-cell datasets and bulk transcriptome profiles, a macrophage-related score (MRS) consisting of the 10-gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial-mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high-MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E-knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.

Basement membrane-associated gene expression as a predictor of survival in oral cancer

BackgroundThis study sought to investigate the prognostic value of basement membrane (BM)-associated gene expressions in oral cancer.MethodsWe harvested and integrated data on BM-associated genes (BMGs), the oral cancer transcriptome, and clinical information from public repositories. After identifying differentially expressed BMGs, we used Cox and Lasso regression analyses to create a BMG-based risk score for overall survival at various intervals. We then validated this score using the GSE42743 cohort as a validation set. The prognostic potential of the risk scores and their relations to clinical features were assessed. Further, we conducted functional pathway enrichment, immune cell infiltration, and immune checkpoint analyses to elucidate the immunological implications and therapeutic potential of the BMG-based risk score and constituent genes. To confirm the expression levels of the BMG LAMA3 in clinical samples of oral cancer tissue, we performed quantitative real-time PCR (qRT-PCR) and immunohistochemical staining.ResultsThe BMGs LAMA3, MMP14, and GPC2 demonstrated notable prognostic significance, facilitating the construction of a BMG-based risk score. A higher risk score derived from BMGs correlated with a poorer survival prognosis for oral cancer patients. Moreover, the risk-associated BMGs exhibited a significant relationship with immune function variability (P < 0.05), discrepancies in infiltrating immune cell fractions, and immune checkpoint expressions (P < 0.05). The upregulated expression levels of LAMA3 in oral cancer tissues were substantiated through qRT-PCR and immunohistochemical staining.ConclusionThe BMG-based risk score emerged as a reliable prognostic tool for oral cancer, meriting further research for validation and potential clinical application.

Effect of ionizing radiation on JAK/STAT pathway and immune microenvironment in cervical cancer.

e17516 Background: STAT proteins play an important role in the development and progression of cervical cancer, and inhibition of the JAK/STAT pathway may be critical for enhancing tumor cell death. Delivering a certain dose of ionizing radiation (IR) directly to the lesion is one of the conventional treatments for cervical cancer. Whether IR will trigger changes in the JAK/STAT pathway and tumor immune microenvironment (TIME) is unknown and continues to be explored. Methods: We collected 30 patients received IR therapy with locally advanced cervical cancer (LACC) at the Affiliated Hospital of Qingdao University. Matched pre-IR and post-IR tumor tissue samples were collected from all patients, and the samples underwent RNA sequencing based on the nCounter platform (NanoString Technologies, Seattle, USA) and protein testing based on immunohistochemistry (IHC) staining. All tumor TIME cell infiltration scores were calculated as arithmetic mean of the constituent genes. P-values were calculated via the T Test. Results: RNA sequencing of tumor tissue from LACC patients before and after IR showed 35 differential genes. The results of GSEA pathway enrichment analysis of differential genes showed that compared with before IR, the IFN-α response pathway, IFN-γ response pathway and epithelial mesenchymal transition pathway were significantly up-regulated and enriched after IR. These three pathways collectively affect changes in the downstream JAK/STAT pathway. Moreover, compared with before IR, the cell infiltration abundance of overall macrophages (P=0.0087) and M2 macrophages (P=0.0043) increased significantly after IR. M1 macrophages also showed an increasing trend, but there was no statistically significant difference. In addition, further verification of changes in several proteins on the JAK/STAT pathway by IHC. The results showed that overall macrophages (P=0.0087) and M1 macrophages (P=0.026), PD-L1 (p=0.003), JAK1 (p=0.021) and STAT3 (p=0.018) were significantly increased after IR compared with before IR. M2 macrophages and STAT1 (p=0.734) also showed an increasing trend, but there was no statistically significant difference. It is worth noting that Kendall's tau-b correlation analysis was used to study the correlation of protein changes. The results found that the changes in PD-L1 expression before and after radiotherapy were related to STAT3 (Kendall's tau-b=0.487, P=0.02), JAK1 (Kendall's tau-b=0.513, P=0.015), there is a strong positive correlation. Conclusions: This study found that IR affects changes in cervical cancer TIME and these changes may promote the transition to an immunosuppressive phenotype. In addition, IR may affect changes in PD-L1 levels through the JAK1/STAT3 pathway. This needs to be further verified by designing large prospective clinical trials in the future.

Blood RNA-sequencing across the continuum of ANA-positive autoimmunity reveals insights into initiating immunopathology

ObjectiveMechanisms underpinning clinical evolution to systemic lupus erythematosus (SLE) from preceding antinuclear antibodies (ANA) positivity are poorly understood. This study aimed to understand blood immune cell transcriptional signatures associated with...

The implications of APOBEC3-mediated C-to-U RNA editing for human disease

Intra-organism biodiversity is thought to arise from epigenetic modification of constituent genes and post-translational modifications of translated proteins. Here, we show that post-transcriptional modifications, like RNA editing, may also contribute. RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosine to uracil. RNAsee (RNA site editing evaluation) is a computational tool developed to predict the cytosines edited by these enzymes. We find that 4.5% of non-synonymous DNA single nucleotide polymorphisms that result in cytosine to uracil changes in RNA are probable sites for APOBEC3A/G RNA editing; the variant proteins created by such polymorphisms may also result from transient RNA editing. These polymorphisms are associated with over 20% of Medical Subject Headings across ten categories of disease, including nutritional and metabolic, neoplastic, cardiovascular, and nervous system diseases. Because RNA editing is transient and not organism-wide, future work is necessary to confirm the extent and effects of such editing in humans.

Cross-evaluation of E. coli’s operon structures via a whole-cell model suggests alternative cellular benefits for low- versus high-expressing operons

Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E.coli's 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper's transparent peer review process is included in the supplemental information.

New mitogenomes from the genus Cricotopus (Diptera: Chironomidae, Orthocladiinae): Characterization and phylogenetic implications.

Cricotopus is a large and diverse genus of non-biting midges composed of several subgenera. Complete mitogenome sequences are available for very few Cricotopus species. The subgenus Pseudocricotopus unites species with unusual morphological structures in adult male and pupal stages, however, molecular methods are needed to verify the placement of this subgenus within Cricotopus. We obtained mitogenomes of C. (Pseudocricotopus) cf. montanus and nine other Cricotopus species for phylogenetic analysis, coupled with two Rheocricotopus species and one Synorthocladius species as outgroups. The structure of the mitogenome was similar among these Cricotopus species, exhibiting A+T bias and retaining ancestral gene order. Mutation rate, estimated as Ka/Ks, varied among genes, and was highest for ATP8 and lowest for COI. The phylogenetic relationships among species of Cricotopus, Rheocricotopus and Synorthocladius was reconstructed using Bayesian inference and maximum likelihood estimation. The phylogenetic trees confirmed placement of subgenus Pseudocricotopus, represented by Cricotopus cf. montanus, within Cricotopus. Our study increases the library of chironomid mitogenomes and provides insight into the properties of their constituent genes.

Phylogenetic Tree Formation Analysis of SARS-CoV-2 ORF3a Protein using Neighbor-Joining

SARS-CoV-2 or coronavirus disease is the cause of the world pandemic in 2019 (Adnan et al., 2020). In its development, SARS-CoV-2 has occurred mutations, resulting in the emergence of several new variants (Hartono and Yusuf, 2021). One of the efforts made to determine the occurrence of mutations is to build and analyze phylogenetic tree (van Dorp et al., 2020). ORF3a protein is one of the constituent genes of the SARS-CoV-2 virus (Ahmad et al., 2022). The dataset was obtained through a database that has been publicly published on the NCBI website (https://www.ncbi.nlm.nih.gov/). The data used are homo sapiens from China (Wu et al., 2020), USA (Daniel et al., 2020), France (Shannon et al., 2020), Indonesia (Rantam et al., 2021), Australia (Caly et al., 2020), and Mexico (Mendoza-Salazar et al., 2022). The phylogenetic tree was constructed using the Neighbor-Joining method. The result is ORF3a from Mexico and France are closely related. ORF3a from Mexico and France have the same level of distance and similarity with ORF3a from China and ORF3a from Indonesia have proximity to ORF3a USA.

Inferring delays in partially observed gene regulation processes.

Cell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality. We develop a simulation-based Bayesian MCMC method employing an approximate likelihood for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: an activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. With prior information about observed fluorescent protein synthesis, our method successfully infers the dynamics of the unobserved regulatory protein. We can estimate the delay and kinetic parameters characterizing target regulation including transcription, translation, and target searching of an unobserved protein from experimental measurements of the products of its target gene. Our method is scalable and can be used to analyze non-Markovian models with hidden components. Our code is implemented in R and is freely available with a simple example data at https://github.com/Mathbiomed/SimMCMC.

Harnessing large language models (LLMs) for candidate gene prioritization and selection

BackgroundFeature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection.MethodsIn this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene’s biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene.ResultsOf the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module.ConclusionsTaken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.

Independent component analysis of Corynebacterium glutamicum transcriptomes reveals its transcriptional regulatory network

Gene expression in bacteria is regulated by multiple transcription factors. Clarifying the regulation mechanism of gene expression is necessary to understand bacterial physiological activities. To further understand the structure of the transcriptional regulatory network of Corynebacterium glutamicum, we applied independent component analysis, an unsupervised machine learning algorithm, to the high-quality C. glutamicum gene expression profile which includes 263 samples from 29 independent projects. We obtained 87 robust independent regulatory modules (iModulons). These iModulons explain 76.7% of the variance in the expression profile and constitute the quantitative transcriptional regulatory network of C. glutamicum. By analyzing the constituent genes in iModulons, we identified potential targets for 20 transcription factors. We also captured the changes in iModulon activities under different growth rates and dissolved oxygen concentrations, demonstrating the ability of iModulons to comprehensively interpret transcriptional responses to environmental changes. In summary, this study provides a genome-scale quantitative transcriptional regulatory network for C. glutamicum and informs future research on complex changes in the transcriptome.

TRAF2 promotes M2-polarized tumor-associated macrophage infiltration, angiogenesis and cancer progression by inhibiting autophagy in clear cell renal cell carcinoma

BackgroundThe management of advanced clear cell renal cell carcinoma (ccRCC) remains a major challenge in clinical practice, and the construction of more reliable prognostic prediction models and the further elucidation of key molecular mechanisms of tumor progression are topics in urgent need of in-depth investigation.MethodsWe used CIBERSORT to estimate the proportion of 22 tumor-infiltrating immune cell types in the TCGA-KIRC cohort. Weighted gene co-expression network analysis, least absolute shrinkage and selection operator regression analysis were used to build risk prediction models. Expression patterns and clinical significance of TRAF2 were determined through bioinformatics analysis, real-time qPCR, Western Blot, immunohistochemistry. GSEA analysis, transmission electron microscopy, 2D/3D colony formation assay, cell migration and invasion assay, and tube-formation assay were used to investigate the underlying function and mechanism of the TRAF2/M2 macrophage/autophagy axis.ResultsWe constructed a novel prognostic prediction model based on M2 macrophage-related genes, which was identified as an accurate, independent and specific prognostic risk model for ccRCC patients. A reliable nomogram was constructed to predict 1-, 3-, and 5-year overall survival for patients with ccRCC. As one of the constituent genes of the risk model, TRAF2 was determined to be upregulated in ccRCC and associated with poor clinical prognosis. We found that TRAF2 promotes malignant progression of ccRCC by regulating macrophage polarization, migration and angiogenesis. Mechanistically, we found that TRAF2 promotes the polarization of M2 macrophages, and this chemotaxis is achieved in an autophagy-dependent pathway. Orthotopic tumor growth assay results revealed that TRAF2 plays a key role as a promotor of ccRCC growth and metastasis.ConclusionsIn conclusion, this risk model is highly predictive of prognostic in ccRCC patients, which is expected to promote improved treatment evaluation and comprehensive management of ccRCC. Moreover, our findings reveal that the TRAF2/M2 macrophage/autophagy axis plays a key regulatory role in the malignant progression of ccRCC, and suggest that TRAF2 is a potential novel therapeutic target for advanced ccRCC.

Tumor immune microenvironment of different lesions in patients with multiple primary lung cancer.

e20549 Background: More and more multiple primary lung cancers (MPLCs) are found clinically. The driver genes and clinical differences between different lesions in MPLCs are obvious, which may affect the choice of subsequent treatment options. As a new type of therapy, immunotherapy has improved the survival of lung cancer patients to a certain extent. However, the efficacy of immunotherapy on different lesions of the same patient with MPLCs is not yet clear. Our study explored the similarities and differences of the tumor immune microenvironment in MPLCs lesions and provided a theoretical basis for immunotherapy in MPLCs. Methods: We collected 20 surgical tumor lesion samples from 10 patients with MPLCs for 289-gene panel RNA sequencing using NanoString nCounter. All tumor tumor immune microenvironment (TIME) cell infiltration scores were calculated as arithmetic mean of the constituent genes. Immune infiltration and expression of immune-related genes in different groups were assessed. Results: All 10 patients in our study were lung adenocarcinoma. Firstly, we classified them using an unsupervised consensus clustering based on TIME-signature gene expression used nonnegative matrix factorization of R package. The results revealed the cluster number of two was optimal (TIME Cluster1: n = 6 patients / 12 lesions; TIME Cluster2: n = 4 patients / 8 lesions). It is worth noting that the two lesions of the same patient are divided into the same group, indicating that the immune microenvironment status of the two lesions of the same person is basically similar. In addition, the nertrophils, NK CD56dim cells and GEP score of Cluster1 is significantly higher than that of Cluster2. Then we grouped them according to gender, ipsilateral or heteroside primary lesions, left or right lung, and pathological infiltration or non-infiltration, and found there was no significant difference in the immune microenvironment between the all the two groups. Conclusions: Dual primary samples from the same individual have similar immune microenvironments and may respond similarly to immunotherapy. Prospective immune-related clinical trials can be designed for further verification in MPLCs patients.

Targeting the NLRP3 inflammasome reduces inflammation in hidradenitis suppurativa skin.

Treatment for the debilitating disease hidradenitis suppurativa (HS) is inadequate in many patients. Despite an incidence of approximately 1%, HS is often underrecognized and underdiagnosed and is associated with a high morbidity and poor quality of life. A better understanding of its pathogenesis is required to design new therapeutic strategies. In this study we employed single cell RNA sequencing to analyse gene expression in immune cells isolated from involved HS skin compared with healthy skin. Flow cytometry was used to quantify the absolute numbers of the main immune populations. Secretion of inflammatory mediators from skin explant cultures was measured using multiplex assays and ELISA. Single cell RNA sequencing analysis identified a significant enrichment in the frequency of plasma cells, Th17 cells and dendritic cell subsets in HS skin, and the immune transcriptome was distinct and much more heterogenous when compared with healthy skin. Flow cytometry revealed significantly increased numbers of T cells, B cells, neutrophils, dermal macrophages, and dendritic cells into involved HS skin. Genes and pathways associated with Th17 cells, IL-17, IL-1β, and the NLRP3 inflammasome were enhanced in HS skin, particularly in samples with a high inflammatory load. Inflammasome constituent genes principally mapped to Langerhans cells and a subpopulation of dendritic cells. The secretome of HS skin explants contained significantly increased concentrations of inflammatory mediators including IL-1β and IL-17A, and culture with an NLRP3 inflammasome inhibitor significantly reduced the secretion of these as well as other key mediators of inflammation. These data provide a rationale for targeting the NLRP3 inflammasome in HS using small molecule inhibitors which are currently being tested for other indications.

Being noisy in a crowd: Differential selective pressure on gene expression noise in model gene regulatory networks.

Expression noise, the variability of the amount of gene product among isogenic cells grown in identical conditions, originates from the inherent stochasticity of diffusion and binding of the molecular players involved in transcription and translation. It has been shown that expression noise is an evolvable trait and that central genes exhibit less noise than peripheral genes in gene networks. A possible explanation for this pattern is increased selective pressure on central genes since they propagate their noise to downstream targets, leading to noise amplification. To test this hypothesis, we developed a new gene regulatory network model with inheritable stochastic gene expression and simulated the evolution of gene-specific expression noise under constraint at the network level. Stabilizing selection was imposed on the expression level of all genes in the network and rounds of mutation, selection, replication and recombination were performed. We observed that local network features affect both the probability to respond to selection, and the strength of the selective pressure acting on individual genes. In particular, the reduction of gene-specific expression noise as a response to stabilizing selection on the gene expression level is higher in genes with higher centrality metrics. Furthermore, global topological structures such as network diameter, centralization and average degree affect the average expression variance and average selective pressure acting on constituent genes. Our results demonstrate that selection at the network level leads to differential selective pressure at the gene level, and local and global network characteristics are an essential component of gene-specific expression noise evolution.

Tumor Microenvironment Composition Correlates with Quantitative 18F-FDG PET-CT Features and Serum TARC in Patients with Relapsed or Refractory Hodgkin Lymphoma

Background Classical Hodgkin lymphoma (cHL) is a heterogeneous disease characterized by a limited number of Hodgkin Reed-Sternberg (HRS) cells in a complex tumor microenvironment (TME). Presence of cHL can be visualized by 18F-FDG PET-CT and can be detected in serum by measuring the HRS cell-derived chemokine TARC. While both these methods have shown prognostic value, a clear link between HRS/TME composition, serum TARC levels, and glucose metabolism measured by PET is lacking. The aim of this study was to assess correlations between gene expression based cell composition in tissue, quantitative PET features and serum TARC in patients with relapsed or refractory (R/R) cHL. Methods Lymph node biopsies, PET scans and serum samples at time of relapse were analyzed from 30 patients with R/R cHL who were treated within a clinical trial (NCT02280993). Immunohistochemical (IHC) staining was done for TARC. Soluble serum TARC was measured by ELISA. RNA was extracted from paraffin embedded lymph node biopsies and the expression of 235 genes related to the TME was measured using NanoString probes. The data were normalized on the geometric mean of 10 housekeeping genes. Genes were assigned to various TME signatures according to the literature (Scott et al., JCO 2012). Signature scores were calculated by taking the geometric mean of its constituent genes. PET scans were analyzed using a threshold of standard uptake value (SUV)4.0, which we have validated previously (Driessen et al., JNM 2021). Metabolic tumor volume (MTV), intensity (e.g. SUVpeak, SUVmean) and distance (D) features (e.g. number of lesions, Dmax, the largest distance between two lesions, and spread, the sum of all distances between lesions) were calculated at patient-level. Correlations were assessed using Spearman's rank correlation coefficients. Unsupervised K-means clustering with 1000 repeats was performed on gene signatures that showed a significant correlation with at least one PET feature. Results As expected, samples with positive TARC IHC staining showed higher TARC (CCL17) expression compared to samples with weak or negative TARC IHC (mean log2 expression 10.2 (95%CI: 9.5-10.8) vs 6.9 (95%CI: 6.2-7.5); p<0.001). The HRS cell signature correlated significantly with serum TARC levels (R=0.54; p=0.002), but not with PET features [Figure 1A]. The NK cell and eosinophil signatures also showed significant correlations with serum TARC levels (R=0.54; p=0.002 and R=0.49; p=0.006, respectively). The macrophage signature correlated significantly with SUVpeak (R=0.39; p=0.039). The antigen presenting cell (APC) signature correlated significantly with serum TARC (R=0.43; p=0.018), SUVpeak (R=0.50; p=0.006) and MTV (R=0.57; p=0.001). Within the APC signature, CD80 and CD40 showed the highest correlations for SUVpeak and MTV. Interestingly, the follicular dendritic cell (FDC) network signature showed significant negative correlations with SUVpeak (R=-0.38; p=0.044), SUVmean (R=-0.43; p=0.019) and MTV (R=-0.37; p=0.046). Unsupervised clustering based on the three gene signatures that showed significant correlations with at least one PET feature (i.e. APC, macrophage and FDC network) resulted in two distinct clusters with a significant difference in MTV and SUVpeak [Figure 1B]. Cluster 1 was associated with low MTV and SUVpeak, showed significant enrichment for the FDC network signature, and showed a trend for enrichment of B cells (p=0.15) and Th2 cells (p=0.15). Cluster 2 was associated with higher MTV and SUVpeak and showed significant enrichment for APCs, macrophages, Th1 cells, resting NK cells, myeloid lineage 1 and 2, neutrophils and viral genes. Conclusions We showed significant correlations between several quantitative PET features such as SUVpeak and MTV with gene signatures of macrophages, APCs and FDC networks, but not with the HRS cell signature. This suggests that TME cell types confer a high glucose metabolism which may explain differences in PET features between patients better than the abundance of HRS cells. These findings can aid in the understanding of the biological correlation of PET-CT scan findings in clinical practice and their relationship to cHL pathophysiology. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Additive seed treatment

The paper states that the problem of developing valuable starting material for breeding new varieties is very acute. In this situation, the authors consider it necessary to offer new methods of inducing aberrant or recombinative plant variability without using GMOs but attracting own hidden reserves of seeds to increase their germination, seedling growth, productivity, and yield of future agricultural products. For this purpose, the research team suggested treating wet seeds with physical factors by mobilizing forces and releasing energy reserves of seeds, activating physiological and biochemical processes at the early stages of their germination. The research showed that the more water is in the seed cells, the more immobile the rotating DNA molecule is and the more vulnerable the constituent genes are subject to irradiation. The authors found that an increase in seed humidity before irradiation led to a higher yield of chromosomal aberrations, expanding the spectrum of their economically valuable traits to be transmitted to the next generations. The authors presented the results of their studies on the effect of electrophysical factors of seed treatment. The procedure included water-thermal seed treatment with alternating electric current and subsequent laser irradiation depending on doses, type, and nature of influences stimulating the growth and development of their seedlings.