Curr Opin Biotechnol. 2023 Oct 6;84:102996. doi: 10.1016/j.copbio.2023.102996. Online ahead of print.

ABSTRACT

The tumor microenvironment (TME) consists of a network of metabolically interconnected tumor and immune cell types. Macrophages influence the metabolic composition within the TME, which directly impacts the metabolic state and drug response of tumors. The accumulation of oncometabolites, such as succinate, fumarate, and 2-hydroxyglutarate, represents metabolic vulnerabilities in cancer that can be targeted therapeutically. Immunometabolites are emerging as metabolic regulators of the TME impacting immune cell functions and cancer cell growth. Here, we discuss recent discoveries on the potential impact of itaconate on the TME. We highlight how itaconate influences metabolic pathways relevant to immune responses and cancer cell proliferation. We also consider the therapeutic implications of manipulating itaconate metabolism as an immunotherapeutic strategy to constrain tumor growth.

PMID:37806082 | DOI:10.1016/j.copbio.2023.102996

Trends Parasitol. 2023 Oct 6:S1471-4922(23)00230-1. doi: 10.1016/j.pt.2023.09.009. Online ahead of print.

ABSTRACT

Recent studies have proposed that Trichomonas vaginalis, the causative agent of trichomoniasis [the most common nonviral sexually transmitted infection (STI) in humans] can establish persistent infections in the vagina. T. vaginalis infections are often asymptomatic but can have adverse consequences such as increased risk of HIV-1 infection and cervical cancer. Despite this, it remains an understudied infection. A potential agent of persistent infections is the 'pseudocyst', a spherical form of T. vaginalis identified by several laboratories and linked to persistence in related species such as the avian parasite Trichomonas gallinae and cattle parasite Tritrichomonas foetus. Additional robust and reproducible research on pseudocysts and persistent T. vaginalis infections is required, which may ultimately shed light on how to better diagnose and treat trichomoniasis.

PMID:37806787 | DOI:10.1016/j.pt.2023.09.009

Mol Ther. 2023 Oct 6:S1525-0016(23)00545-2. doi: 10.1016/j.ymthe.2023.10.004. Online ahead of print.

ABSTRACT

In vivo apoptosis of human mesenchymal stromal cells (MSCs) plays a critical role in delivering immunomodulation. Yet, caspase activity not only mediates the dying process but also death-independent functions that may shape the immunogenicity of apoptotic cells. Therefore, a better characterization of the immunological profile of apoptotic MSCs (ApoMSCs) could shed light on their mechanistic action and therapeutic applications. We analyzed the transcriptomes of MSCs undergoing apoptosis and identified several immunomodulatory factors and chemokines dependent on caspase activation following Fas stimulation. The ApoMSC secretome inhibited human T cell proliferation and activation, and chemoattracted monocytes in vitro. Both immunomodulatory activities were dependent on the cyclooxygenase2 (COX2)/prostaglandin E2 (PGE2) axis. To assess the clinical relevance of ApoMSC signature, we used the peripheral blood mononuclear cells (PBMCs) from a cohort of fistulizing Crohn's Disease (CD) patients who had undergone MSC treatment (ADMIRE-CD). Compared to healthy donors, MSCs exposed to patients' PBMCs underwent apoptosis and released PGE2 in a caspase-dependent manner. Both PGE2 and apoptosis were significantly associated with clinical responses to MSCs. Our findings identify a new mechanism whereby caspase activation delivers ApoMSC immunosuppression. Remarkably, such molecular signatures could implicate translational tools for predicting patients' clinical responses to MSC therapy in CD.

PMID:37805713 | DOI:10.1016/j.ymthe.2023.10.004

Sci Data. 2023 Oct 7;10(1):681. doi: 10.1038/s41597-023-02558-5.

ABSTRACT

Zoonotic spillover of sarbecoviruses (SarbeCoVs) from non-human animals to humans under natural conditions has led to two large-scale pandemics, the severe acute respiratory syndrome (SARS) pandemic in 2003 and the ongoing COVID-19 pandemic. Knowledge of the genetic diversity, geographical distribution, and host specificity of SarbeCoVs is therefore of interest for pandemic surveillance and origin tracing of SARS-CoV and SARS-CoV-2. This study presents a comprehensive repository of publicly available animal-associated SarbeCoVs, covering 1,535 viruses identified from 63 animal species distributed in 43 countries worldwide (as of February 14,2023). Relevant meta-information, such as host species, sampling time and location, was manually curated and included in the dataset to facilitate further research on the potential patterns of viral diversity and ecological characteristics. In addition, the dataset also provides well-annotated sequence sets of receptor-binding domains (RBDs) and receptor-binding motifs (RBMs) for the scientific community to highlight the potential determinants of successful cross-species transmission that could be aid in risk estimation and strategic design for future emerging infectious disease control and prevention.

PMID:37805633 | DOI:10.1038/s41597-023-02558-5

Sci Rep. 2023 Oct 7;13(1):16906. doi: 10.1038/s41598-023-43948-4.

ABSTRACT

The design of popular disposable electronic cigarettes (ECs) was analyzed, and the concentrations of WS-23, a synthetic coolant, in EC fluids were determined for 22 devices from 4 different brands. All products contained WS-23 in concentrations that ranged from 1.0 to 40.1 mg/mL (mean = 21.4 ± 9.2 mg/mL). To determine the effects of WS-23 on human bronchial epithelium in isolation of other chemicals, we exposed EpiAirway 3-D microtissues to WS-23 at the air liquid interface (ALI) using a cloud chamber that generated aerosols without heating. Proteomics analysis of exposed tissues revealed that the cytoskeleton was a major target of WS-23. BEAS-2B cells were exposed to WS-23 in submerged culture to validate the main results from proteomics. F-actin, which was visualized with phalloidin, decreased concentration dependently in WS-23 treated BEAS-2B cells, and cells became immotile in concentrations above 1.5 mg/mL. Gap closure, which depends on both cell proliferation and migration, was inhibited by 0.45 mg/mL of WS-23. These data show that WS-23 is being added to popular EC fluids at concentrations that can impair processes dependent on the actin cytoskeleton and disturb homeostasis of the bronchial epithelium. The unregulated use of WS-23 in EC products may harm human health.

PMID:37805554 | DOI:10.1038/s41598-023-43948-4

Environ Res. 2023 Oct 5:117117. doi: 10.1016/j.envres.2023.117117. Online ahead of print.

ABSTRACT

INTRODUCTION: Colorectal cancer (CRC) is one of the most malignant tumors and in which various efforts for screening is inconclusive.The intracrine FGF panel, the non-tyrosine kinase receptors (NTKR) FGFs and affiliated antisenses play a pivotal role in FGF signaling.The expression levels of coding and non-coding intracrine FGFs were assessed in CRC donors.Also, substantial costs and slow pace of drug discovery give high attraction to repurpose of previously discovered drugs to new opportunities.

OBJECTIVES: The aim of present study was to evaluate the potential role of the coding and non-coding intracrine FGFs as a new biomarkers for CRC cases and defining drug repurposing to alleviate FGF down regulation.

METHODS: RNA-seq data of colon adenocarcinomas (COAD) was downloaded using TCGA biolinks package in R.The DrugBank database (https://go.drugbank.com/) was used to extract interactions between drugs and candidate genes. A total of 200 CRC patients with detailed criteria were enrolled.RNAs were extracted with TRIzol-based protocol and amplified via LightCycler® instrument.FGF11 and FGF13 proteins validation was performed by used of immunohistochemistry technique in tumor and non-tumoral samples.Pearson's correlation analysis and ROC curve plotted by Prism 8.0 software.

RESULTS: RNA-seq data from TCGA was analyzed by normalizing with edgeR.Differentially expressed gene (DEG) analysis was generated. WCC algorithm extracted the most significant genes with a total of 47 genes. Expression elevation of iFGF antisenses (12AS,13As,14AS) compared with the normal colon tissue were observed (P = 0.0003,P = 0.042,P = 0.026, respectively). Moreover,a significant decrease in expression of the corresponding sense iFGF genes was detected (P < 0.0001).Plotted receiver operating characteristic (ROC) curves for iFGF components' expression showed an area of over 0.70 (FGF11-13: 0.71% and FGF12-14: 0.78%, P < 0.001) for sense mRNA expression, with the highest sensitivity for FGF12 (92.8%) and lowest for FGF11 (61.41%).The artificial intelligence (AI) revealed the valproic acid as a repurposing drug to relief the down regulation of FGF12 and 13 in CRC patients.

CONCLUSION: Intracrine FGFs panel was down regulated versus up regulation of dependent antisenses. Thus, developing novel biomarkers based on iFGF can be considered as a promising strategy for CRC screening.In advanced, valporic acid detected by AI as a repurposing drug which may be applied in clinical trials for CRC treatment.

PMID:37805185 | DOI:10.1016/j.envres.2023.117117

J Theor Biol. 2023 Oct 5:111632. doi: 10.1016/j.jtbi.2023.111632. Online ahead of print.

ABSTRACT

Elementary flux modes (EFMs) are minimal, steady state pathways characterizing a flux network. Fundamentally, all steady state fluxes in a network are decomposable into a linear combination of EFMs. While there is typically no unique set of EFM weights that reconstructs these fluxes, several optimization-based methods have been proposed to constrain the solution space by enforcing some notion of parsimony. However, it has long been recognized that optimization-based approaches may fail to uniquely identify EFM weights and return different feasible solutions across objective functions and solvers. Here we show that, for flux networks only involving single molecule transformations, these problems can be avoided by imposing a Markovian constraint on EFM weights. Our Markovian constraint guarantees a unique solution to the flux decomposition problem, and that solution is arguably more biophysically plausible than other solutions. We describe an algorithm for computing Markovian EFM weights via steady state analysis of a certain discrete-time Markov chain, based on the flux network, which we call the cycle-history Markov chain. We demonstrate our method with a differential analysis of EFM activity in a lipid metabolic network comparing healthy and Alzheimer's disease patients. Our method is the first to uniquely decompose steady state fluxes into EFM weights for any unimolecular metabolic network.

PMID:37804942 | DOI:10.1016/j.jtbi.2023.111632

Cell Metab. 2023 Sep 29:S1550-4131(23)00341-8. doi: 10.1016/j.cmet.2023.09.010. Online ahead of print.

ABSTRACT

The intestinal epithelium has a high turnover rate and constantly renews itself through proliferation of intestinal crypt cells, which depends on insufficiently characterized signals from the microenvironment. Here, we showed that colonic macrophages were located directly adjacent to epithelial crypt cells in mice, where they metabolically supported epithelial cell proliferation in an mTORC1-dependent manner. Specifically, deletion of tuberous sclerosis complex 2 (Tsc2) in macrophages activated mTORC1 signaling that protected against colitis-induced intestinal damage and induced the synthesis of the polyamines spermidine and spermine. Epithelial cells ingested these polyamines and rewired their cellular metabolism to optimize proliferation and defense. Notably, spermine directly stimulated proliferation of colon epithelial cells and colon organoids. Genetic interference with polyamine production in macrophages altered global polyamine levels in the colon and modified epithelial cell proliferation. Our results suggest that macrophages act as "commensals" that provide metabolic support to promote efficient self-renewal of the colon epithelium.

PMID:37804836 | DOI:10.1016/j.cmet.2023.09.010

Cell. 2023 Sep 26:S0092-8674(23)01032-2. doi: 10.1016/j.cell.2023.09.011. Online ahead of print.

ABSTRACT

Metabolic reprogramming is a hallmark of cancer. However, mechanisms underlying metabolic reprogramming and how altered metabolism in turn enhances tumorigenicity are poorly understood. Here, we report that arginine levels are elevated in murine and patient hepatocellular carcinoma (HCC), despite reduced expression of arginine synthesis genes. Tumor cells accumulate high levels of arginine due to increased uptake and reduced arginine-to-polyamine conversion. Importantly, the high levels of arginine promote tumor formation via further metabolic reprogramming, including changes in glucose, amino acid, nucleotide, and fatty acid metabolism. Mechanistically, arginine binds RNA-binding motif protein 39 (RBM39) to control expression of metabolic genes. RBM39-mediated upregulation of asparagine synthesis leads to enhanced arginine uptake, creating a positive feedback loop to sustain high arginine levels and oncogenic metabolism. Thus, arginine is a second messenger-like molecule that reprograms metabolism to promote tumor growth.

PMID:37804830 | DOI:10.1016/j.cell.2023.09.011

Pathol Res Pract. 2023 Oct 2;251:154838. doi: 10.1016/j.prp.2023.154838. Online ahead of print.

ABSTRACT

The interaction between long non-coding RNAs (lncRNAs), miRNAs and mRNAs has implications in the pathogenesis of different cancer, including breast cancer. In the current study, we developed an in-silico approach to ascertain the competing endogenous RNA (ceRNA) network in breast cancer. Our approach led to identification of 1816 differentially expressed (DE) mRNAs, including 1039 downregulated DEmRNAs (such as LEP and ADIPOQ) and 777 upregulated DEmRNAs (such as COL11A1 and COL10A1), 19 DElncRNAs, including 15 downregulated DElncRNAs (such as CARMN and COPG2IT1) and 4 upregulated DElncRNAs (such as MALAT1 and NRAV) and 27 DEmiRNAs, including 15 downregulated DEmiRNAs (such as MIR452 and MIR224) and 12 upregulated DEmiRNAs (such as MIR6787 and MIR21). Pathway analysis revealed down-regulation of PPAR, Fatty acid metabolism, Adipocytokine, Vascular smooth muscle contraction and Metabolism of xenobiotics by cytochrome P450, while up-regulation of Pyrimidine metabolism, p53 signaling pathway, Cell cycle, Oocyte meiosis and RNA transport pathways in breast cancer. Finally, we constructed an lncRNA/miRNA/mRNA ceRNA network consisted of 2 lncRNAs, 15 mRNAs, and 4 miRNAs. This network represents an appropriate target for design of anti-cancer modalities and documentation of novel markers for breast cancer.

PMID:37804544 | DOI:10.1016/j.prp.2023.154838

Plant Physiol. 2023 Oct 7:kiad535. doi: 10.1093/plphys/kiad535. Online ahead of print.

ABSTRACT

During photosynthesis, plants must manage strong fluctuations in light availability on different time scales, leading to long-term acclimation and short-term responses. However, little is known about the regulation and coordination of these processes and the modulators involved. In this study, we used proteomics, metabolomics, and reverse genetics to investigate how different light environmental factors, such as intensity or variability, affect long-term and short-term acclimation responses of Arabidopsis (Arabidopsis thaliana) and the importance of the chloroplast redox network in their regulation. In the wild type, high light, but not fluctuating light, led to large quantitative changes in the proteome and metabolome, accompanied by increased photosynthetic dynamics and plant growth. This finding supports light intensity as a stronger driver for acclimation than variability. Deficiencies in NADPH-thioredoxin reductase C (NTRC) or thioredoxins m1/m2, but not thioredoxin f1, almost completely suppressed the reengineering of the proteome and metabolome, with both the induction of proteins involved in stress and redox responses and the repression of those involved in cytosolic and plastid protein synthesis and translation being strongly attenuated. Moreover, the correlations of protein or metabolite levels with light intensity were severely disturbed, suggesting a general defect in the light-dependent acclimation response, resulting in impaired photosynthetic dynamics. These results indicate a previously unknown role of NTRC and thioredoxins m1/m2 in modulating light acclimation at proteome and metabolome levels to control dynamic light responses. NTRC, but not thioredoxins m1/m2 or f1, also improves short-term photosynthetic responses by balancing the Calvin-Benson cycle in fluctuating light.

PMID:37804523 | DOI:10.1093/plphys/kiad535

Microb Biotechnol. 2023 Oct 7. doi: 10.1111/1751-7915.14346. Online ahead of print.

ABSTRACT

Iron is an essential element for all eukaryote organisms because of its redox properties, which are important for many biological processes such as DNA synthesis, mitochondrial respiration, oxygen transport, lipid, and carbon metabolism. For this reason, living organisms have developed different strategies and mechanisms to optimally regulate iron acquisition, transport, storage, and uptake in different environmental responses. Moreover, iron plays an essential role during microbial infections. Saccharomyces cerevisiae has been of key importance for decrypting iron homeostasis and regulation mechanisms in eukaryotes. Specifically, the transcription factors Aft1/Aft2 and Yap5 regulate the expression of genes to control iron metabolism in response to its deficiency or excess, adapting to the cell's iron requirements and its availability in the environment. We also review which iron-related virulence factors have the most common fungal human pathogens (Aspergillus fumigatus, Cryptococcus neoformans, and Candida albicans). These factors are essential for adaptation in different host niches during pathogenesis, including different fungal-specific iron-uptake mechanisms. While being necessary for virulence, they provide hope for developing novel antifungal treatments, which are currently scarce and usually toxic for patients. In this review, we provide a compilation of the current knowledge about the metabolic response to iron deficiency and excess in fungi.

PMID:37804207 | DOI:10.1111/1751-7915.14346

J Am Heart Assoc. 2023 Oct 7:e029040. doi: 10.1161/JAHA.122.029040. Online ahead of print.

ABSTRACT

BACKGROUND Mendelian randomization (MR) offers a powerful approach to study potential causal associations between exposures and health outcomes by using genetic variants associated with an exposure as instrumental variables. In this systematic review, we aimed to summarize previous MR studies and to evaluate the evidence for causality for a broad range of exposures in relation to coronary artery disease and stroke. METHODS AND RESULTS MR studies investigating the association of any genetically predicted exposure with coronary artery disease or stroke were identified. Studies were classified into 4 categories built on the significance of the main MR analysis results and its concordance with sensitivity analyses, namely, robust, probable, suggestive, and insufficient. Studies reporting associations that did not perform any sensitivity analysis were classified as nonevaluable. We identified 2725 associations eligible for evaluation, examining 535 distinct exposures. Of them, 141 were classified as robust, 353 as probable, 110 as suggestive, and 926 had insufficient evidence. The most robust associations were observed for anthropometric traits, lipids, and lipoproteins and type 2 diabetes with coronary artery; disease and clinical measurements with coronary artery disease and stroke; and thrombotic factors with stroke. CONCLUSIONS Despite the large number of studies that have been conducted, only a limited number of associations were supported by robust evidence. Approximately half of the studies reporting associations presented an MR sensitivity analysis along with the main analysis that further supported the causality of associations. Future research should focus on more thorough assessments of sensitivity MR analyses and further assessments of mediation effects or nonlinearity of associations.

PMID:37804188 | DOI:10.1161/JAHA.122.029040

Plant Cell. 2023 Oct 6:koad256. doi: 10.1093/plcell/koad256. Online ahead of print.

ABSTRACT

L-Serine (Ser) and L-Glycine (Gly) are critically important for the overall functioning of primary metabolism. We investigated the interaction of the Phosphorylated Pathway of Ser Biosynthesis (PPSB) with the photorespiration-associated Glycolate Pathway of Ser Biosynthesis (GPSB) using Arabidopsis thaliana PPSB-deficient lines, GPSB-deficient mutants, and crosses of PPSB with GPSB mutants. PPSB-deficient lines mainly showed retarded primary root growth. Mutation of the photorespiratory enzyme Ser-hydroxymethyltransferase 1 (SHMT1) in a PPSB-deficient background resumed primary root growth and induced a change in the plant metabolic pattern between roots and shoots. Grafting experiments demonstrated that metabolic changes in shoots were responsible for the changes in double mutant development. PPSB disruption led to a reduction in nitrogen (N) and sulfur (S) contents in shoots and a general transcriptional response to nutrient deficiency. Disruption of SHMT1 boosted the Gly flux out of the photorespiratory cycle, which increased the levels of the one-carbon (1C) metabolite 5,10-methylene-tetrahydrofolate and S-adenosylmethionine. Furthermore, disrupting SHMT1 reverted the transcriptional response to N and S deprivation and increased N and S contents in shoots of PPSB-deficient-lines. Our work provides genetic evidence of the biological relevance of the Ser-Gly-1C metabolic network in N and S metabolism and in interorgan metabolic homeostasis.

PMID:37804096 | DOI:10.1093/plcell/koad256

J Coll Physicians Surg Pak. 2023 Oct;33(10):1093-1099. doi: 10.29271/jcpsp.2023.10.1093.

ABSTRACT

OBJECTIVE: To analyse potential molecular mechanisms and identify potential therapeutic regimens and drugs to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Crohn's disease (CD) through bioinformatics and systems biology.

STUDY DESIGN: Bioinformatics Study. Place and Duration of the Study: Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China, from May to December 2022.

METHODOLOGY: The common differentially expressed genes (DEGs) between CD and SARS-CoV-2 infection were identified using two RNA-seq datasets (GSE147507, GSE153974) extracted from Gene Expression Omnibus (GEO). Subsequently, functional enrichment, pathway analysis, and candidate drug analysis were performed using these DEGs.

RESULTS: In total, 44 DEGs were identified as common between CD and SARS-CoV-2 infection. A protein-protein interaction (PPI) network was constructed, hub genes were identified, and critical modules were determined by means of bioinformatics and combinatorial statistical approaches. Functional and pathway analyses conducted under ontological conditions showed a common association between CD and infection with SARS-CoV-2. The common DEGs were then used to identify coregulatory networks of interactions between transcriptional factors and genes, between proteins and medicines, and between DEGs and miRNAs.

CONCLUSION: Top 10 hub genes including IL6, CXCL1, CSF2, CXCL2, CXCL5, MMP3, PTGS2, CXCL3, SELE, and LCN2 were identified, which may function as potential candidate targets for SARS-CoV-2 infection. Additionally, the identification of certain promising treatment drugs for patients with SARS-CoV-2 infection and CD was also made.

KEY WORDS: SARS-CoV-2, COVID-19, Drug molecule, Hub gene, Protein-protein interaction (PPI), Gene ontology, Crohn's disease, Differentially expressed genes.

PMID:37804012 | DOI:10.29271/jcpsp.2023.10.1093

Metab Eng. 2023 Oct 4:S1096-7176(23)00140-4. doi: 10.1016/j.ymben.2023.09.014. Online ahead of print.

ABSTRACT

Quantification of how different environmental cues affect protein allocation can provide important insights for understanding cell physiology. While absolute quantification of proteins can be obtained by resource-intensive mass-spectrometry-based technologies, prediction of protein abundances offers another way to obtain insights into protein allocation. Here we present CAMEL, a framework that couples constraint-based modelling with machine learning to predict protein abundance for any environmental condition. This is achieved by building machine learning models that leverage static features, derived from protein sequences, and condition-dependent features predicted from protein-constrained metabolic models. Our findings demonstrate that CAMEL results in excellent prediction of protein allocation in E. coli (average Pearson correlation of at least 0.9), and moderate performance in S. cerevisiae (average Pearson correlation of at least 0.5). Therefore, CAMEL outperformed contending approaches without using molecular read-outs from unseen conditions and provides a valuable tool for using protein allocation in biotechnological applications.

PMID:37802292 | DOI:10.1016/j.ymben.2023.09.014

Adv Nutr. 2023 Oct 4:S2161-8313(23)01388-1. doi: 10.1016/j.advnut.2023.09.015. Online ahead of print.

ABSTRACT

Human milk (HM) contains macronutrients, micronutrients and a multitude of other bioactive factors, which can have a long-term impact on infant growth and development. We systematically searched Medline, EMBASE, Cochrane Library, Scopus, and Web of Science to synthesize evidence published between 1980-2022 on HM components and anthropometry through 2 years of age among term-born infants. From 9,992 abstracts screened, 141 articles were included and categorized based on their reporting of HM micronutrients, macronutrients, or bioactive components. Bioactives including hormones, HM oligosaccharides (HMOs), and immunomodulatory components are reported here, based on 75 articles from 69 unique studies reporting observations from 9,980 dyads. Research designs, milk collection strategies, sampling times, geographic and socioeconomic settings, reporting practices, and outcomes varied considerably. Meta-analyses were not possible because data collection times and reporting were inconsistent amongst the studies included. Few measured infant HM intake, adjusted for confounders, precisely captured breastfeeding exclusivity, or adequately described HM collection protocols. Only five studies (6%) had high overall quality scores. Hormones were the most extensively examined bioactive with 46 articles (n=6773 dyads), compared to 13 (n=2,640 dyads) for HMOs, and 12 (n=1422 dyads) for immunomodulatory components. Two studies conducted untargeted metabolomics. Leptin and adiponectin demonstrated inverse associations with infant growth, although several studies found no associations. No consistent associations were found between individual HMOs and infant growth outcomes. Among immunomodulatory components in HM, interleukin-6 (IL-6) demonstrated inverse relationships with infant growth. Current research on HM bioactives is largely inconclusive and is insufficient to address the complex composition of HM. Future research should ideally capture human milk intake, use biologically relevant anthropometrics, and integrate components across categories, embracing a systems biology approach to better understand how HM components work independently and synergistically to influence infant growth.

PMID:37802214 | DOI:10.1016/j.advnut.2023.09.015

IMA Fungus. 2023 Oct 6;14(1):21. doi: 10.1186/s43008-023-00121-w.

NO ABSTRACT

PMID:37803441 | DOI:10.1186/s43008-023-00121-w

Nat Methods. 2023 Oct;20(10):1441-1442. doi: 10.1038/s41592-023-02023-6.

NO ABSTRACT

PMID:37803226 | DOI:10.1038/s41592-023-02023-6

Methods Mol Biol. 2024;2719:51-77. doi: 10.1007/978-1-0716-3461-5_4.

ABSTRACT

Growth is regulated by gene expression variation at different developmental stages of biological processes such as cell differentiation, disease progression, or drug response. In cancer, a stage-specific regulatory model constructed to infer the dynamic expression changes in genes contributing to tissue growth or proliferation is referred as a dynamic growth regulatory network (dGRN). Over the past decade, gene expression data has been widely used for reconstructing dGRN by computing correlations between the differentially expressed genes (DEGs). A wide variety of pipelines are available to construct the GRNs using DEGs and the choice of a particular method or tool depends on the nature of the study. In this protocol, we have outlined a step-by-step guide for the analysis of DEGs using RNA-Seq data, beginning from data acquisition, pre-processing, mapping to reference genome, and construction of a correlation-based co-expression network to further downstream analysis. We have also outlined the steps for the inclusion of publicly available interaction/regulation information into the dGRN followed by relevant topological inferences. This tutorial has been designed in a way that early researchers can refer to for an easy and comprehensive glimpse of methodologies used in the inference of dGRN using transcriptomics data.

PMID:37803112 | DOI:10.1007/978-1-0716-3461-5_4