Transcriptional Regulation: Molecules, Involved Mechanisms, and Misregulation

CRISPR/Cas9-based genome-wide screening for metastasis ability identifies FCGR1A regulating the metastatic process of ovarian cancer by targeting LSP1

BACKGROUND

Metastasis is a main cause of death from ovarian cancer (OC). Identifying key markers involved in OC metastasis can aid in the effective detection of early postoperative metastasis. However, the role of FCGR1A in OC metastasis has yet to be fully established. A genome-wide CRISPR/Cas9-based screening system was used to identify regulatory factors involved in metastasis.

METHODS

The expression of FCGR1A and LSP1 in ovarian cancer cell lines was examined by quantitative real-time polymerase chain reaction (qRT‒PCR). The functions of FCGR1A and LSP1 in OC cell migration, invasion and proliferation were determined using wound healing, Transwell invasion and CKK-8 assays. A transcription-activated library was used to identify the potential downstream genes of FCGR1A. FCGR1A expression was detected by immunohistochemistry and the immunity risk score (IRS) scores were calculated.

RESULTS

FCGR1A was upregulated in OC cells compared with normal ovarian cells. Downregulation of FCGR1A inhibited metastasis, proliferation and epithelial-mesenchymal transition (EMT) progression in OC cells in vitro and intraperitoneal metastasis in vivo. Moreover, downregulation of FCGR1A was accompanied by decreased LSP1 expression. Overexpression of LSP1 partially reversed the tumor suppressive effect of FCGR1A downregulation. Higher FCGR1A expression was related to metastasis, higher grade, higher stage, and lymph node metastasis in OC. Survival analysis suggested that the group with higher FCGR1A expression had a lower tumor-free survival rate and a lower overall survival rate than did the group with low FCGR1A expression.

CONCLUSIONS

FCGR1A enhances OC metastasis by regulating LSP1, and FCGR1A is associated with poor prognosis, suggesting that FCGR1A is a potential predictive factor for detecting early postoperative metastasis.

Enigmatic role of auxin response factors in plant growth and stress tolerance

Abiotic and biotic stresses globally constrain plant growth and impede the optimization of crop productivity. The phytohormone auxin is involved in nearly every aspect of plant development. Auxin acts as a chemical messenger that influences gene expression through a short nuclear pathway, mediated by a family of specific DNA-binding transcription factors known as Auxin Response Factors (ARFs). ARFs thus act as effectors of auxin response and translate chemical signals into the regulation of auxin responsive genes. Since the initial discovery of the first ARF in Arabidopsis, advancements in genetics, biochemistry, genomics, and structural biology have facilitated the development of models elucidating ARF action and their contributions to generating specific auxin responses. Yet, significant gaps persist in our understanding of ARF transcription factors despite these endeavors. Unraveling the functional roles of ARFs in regulating stress response, alongside elucidating their genetic and molecular mechanisms, is still in its nascent phase. Here, we review recent research outcomes on ARFs, detailing their involvement in regulating leaf, flower, and root organogenesis and development, as well as stress responses and their corresponding regulatory mechanisms: including gene expression patterns, functional characterization, transcriptional, post-transcriptional and post- translational regulation across diverse stress conditions. Furthermore, we delineate unresolved questions and forthcoming challenges in ARF research.

BIT: Bayesian Identification of Transcriptional Regulators

BIT is a novel Bayesian hierarchical model capable of predicting transcriptional regulators (TRs) from the input of user-provided epigenomic regions. TRs are critical molecules in transcriptional regulation. Many diseases and cancers are linked to the dysfunction of TRs. Knowing TRs in certain biological process can help find new biomarkers or therapeutic targets. Thus, BIT formulates a novel Bayesian hierarchical model with the Pólya-gamma data augmentation strategy. Based on collected ChIP-seq datasets, BIT can identify TRs responsible for the genome-wide binding pattern within the user-provided epigenomic regions. BIT has been validated by using a simulation study and three applications.

Role of epigenetics and alterations in RNA metabolism in leukodystrophies

Leukodystrophies are a class of rare heterogeneous disorders which affect the white matter of the brain, ultimately leading to a disruption in brain development and a damaging effect on cognitive, motor and social-communicative development. These disorders present a great clinical heterogeneity, along with a phenotypic overlap and this could be partially due to contributions from environmental stimuli. It is in this context that there is a great need to investigate what other factors may contribute to both disease insurgence and phenotypical heterogeneity, and novel evidence are raising the attention toward the study of epigenetics and transcription mechanisms that can influence the disease phenotype beyond genetics. Modulation in the epigenetics machinery including histone modifications, DNA methylation and non-coding RNAs dysregulation, could be crucial players in the development of these disorders, and moreover an aberrant RNA maturation process has been linked to leukodystrophies. Here, we provide an overview of these mechanisms hoping to supply a closer step toward the analysis of leukodystrophies not only as genetically determined but also with an added level of complexity where epigenetic dysregulation is of key relevance. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNA RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.

Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

BACKGROUND

Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features.

RESULTS

We propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC).

CONCLUSION

Here we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.

Global Run-On sequencing to measure nascent transcription in Saccharomyces cerevisiae

Global Run-On sequencing is a reliable and widely used approach for monitoring nascent transcription on a genomewide scale. The assay has been successfully used for studying global transcription in humans, plants, worms, flies, and fission yeast. Here we describe a GRO-seq protocol for studying transcription in budding yeast, Saccharomyces cerevisiae. Briefly, the technique involves permeabilization of actively growing yeast cells, allowing transcription to proceed in permeabilized cells in the presence of brominated UTP, affinity purification of bromo-UMP incorporated nascent transcripts followed by cDNA library construction, deep sequencing, and mapping against the reference genome. The approach maps the position of transcriptionally active RNA polymerase on a genomewide basis. In addition to identifying the complete set of transcriptionally active genes in a cell under a given set of conditions, the method can be used to determine elongation rate, termination defect and promoter directionality at the genomewide level. The approach is especially useful in identifying short-lived unstable transcripts that are rapidly degraded even before they leave the nucleus.

Expression of mTOR in normal and pathological conditions

The mechanistic/mammalian target of rapamycin (mTOR), a protein discovered in 1991, integrates a complex pathway with a key role in maintaining cellular homeostasis. By comprising two functionally distinct complexes, mTOR complex 1 (mTORC1) and mTORC2, it is a central cellular hub that integrates intra- and extracellular signals of energy, nutrient, and hormone availability, modulating the molecular responses to acquire a homeostatic state through the regulation of anabolic and catabolic processes. Accordingly, dysregulation of mTOR pathway has been implicated in a variety of human diseases. While major advances have been made regarding the regulators and effectors of mTOR signaling pathway, insights into the regulation of mTOR gene expression are beginning to emerge. Here, we present the current available data regarding the mTOR expression regulation at the level of transcription, translation and mRNA stability and systematize the current knowledge about the fluctuations of mTOR expression observed in several diseases, both cancerous and non-cancerous. In addition, we discuss whether mTOR expression changes can be used as a biomarker for diagnosis, disease progression, prognosis and/or response to therapeutics. We believe that our study will contribute for the implementation of new disease biomarkers based on mTOR as it gives an exhaustive perspective about the regulation of mTOR gene expression in both normal and pathological conditions.

Intergenic transcription in in vivo developed bovine oocytes and pre-implantation embryos

Background

Intergenic transcription, either failure to terminate at the transcription end site (TES), or transcription initiation at other intergenic regions, is present in cultured cells and enhanced in the presence of stressors such as viral infection. Transcription termination failure has not been characterized in natural biological samples such as pre-implantation embryos which express more than 10,000 genes and undergo drastic changes in DNA methylation.

Results

Using Automatic Readthrough Transcription Detection (ARTDeco) and data of in vivo developed bovine oocytes and embryos, we found abundant intergenic transcripts that we termed as read-outs (transcribed from 5 to 15 kb after TES) and read-ins (transcribed 1 kb up-stream of reference genes, extending up to 15 kb up-stream). Read-throughs (continued transcription from TES of expressed reference genes, 4-15 kb in length), however, were much fewer. For example, the numbers of read-outs and read-ins ranged from 3,084 to 6,565 or 33.36-66.67% of expressed reference genes at different stages of embryo development. The less copious read-throughs were at an average of 10% and significantly correlated with reference gene expression (P < 0.05). Interestingly, intergenic transcription did not seem to be random because many intergenic transcripts (1,504 read-outs, 1,045 read-ins, and 1,021 read-throughs) were associated with common reference genes across all stages of pre-implantation development. Their expression also seemed to be regulated by developmental stages because many were differentially expressed (log2 fold change ≥ 2, P < 0.05). Additionally, while gradual but un-patterned decreases in DNA methylation densities 10 kb both up- and down-stream of the intergenic transcribed regions were observed, the correlation between intergenic transcription and DNA methylation was insignificant. Finally, transcription factor binding motifs and polyadenylation signals were found in 27.2% and 12.15% of intergenic transcripts, respectively, suggesting considerable novel transcription initiation and RNA processing.

Conclusion

In summary, in vivo developed oocytes and pre-implantation embryos express large numbers of intergenic transcripts, which are not related to the overall DNA methylation profiles either up- or down-stream.

Transcriptional Regulation and Its Misregulation in Human Diseases

Transcriptional regulation is a critical biological process that allows the cell or an organism to respond to a variety of intra- and extracellular signals, to define cell identity during development, to maintain it throughout its lifetime, and to coordinate cellular activity [...].

A Novel Molecular Classification Method for Glioblastoma Based on Tumor Cell Differentiation Trajectories

The latest 2021 WHO classification redefines glioblastoma (GBM) as the hierarchical reporting standard by eliminating glioblastoma, IDH-mutant and only retaining the tumor entity of "glioblastoma, IDH-wild type." Knowing that subclassification of tumors based on molecular features is supposed to facilitate the therapeutic choice and increase the response rate in cancer patients, it is necessary to carry out molecular classification of the newly defined GBM. Although differentiation trajectory inference based on single-cell sequencing (scRNA-seq) data holds great promise for identifying cell heterogeneity, it has not been used in the study of GBM molecular classification. Single-cell transcriptome sequencing data from 10 GBM samples were used to identify molecular classification based on differentiation trajectories. The expressions of identified features were validated by public bulk RNA-sequencing data. Clinical feasibility of the classification system was examined in tissue samples by immunohistochemical (IHC) staining and immunofluorescence, and their clinical significance was investigated in public cohorts and clinical samples with complete clinical follow-up information. By analyzing scRNA-seq data of 10 GBM samples, four differentiation trajectories from the glioblastoma stem cell-like (GSCL) cluster were identified, based on which malignant cells were classified into five characteristic subclusters. Each cluster exhibited different potential drug sensitivities, pathways, functions, and transcriptional modules. The classification model was further examined in TCGA and CGGA datasets. According to the different abundance of five characteristic cell clusters, the patients were classified into five groups which we named Ac-G, Class-G, Neo-G, Opc-G, and Undiff-G groups. It was found that the Undiff-G group exhibited the worst overall survival (OS) in both TCGA and CGGA cohorts. In addition, the classification model was verified by IHC staining in 137 GBM samples to further clarify the difference in OS between the five groups. Furthermore, the novel biomarkers of glioblastoma stem cells (GSCs) were also described. In summary, we identified five classifications of GBM and found that they exhibited distinct drug sensitivities and different prognoses, suggesting that the new grouping system may be able to provide important prognostic information and have certain guiding significance for the treatment of GBM, and identified the GSCL cluster in GBM tissues and described its characteristic program, which may help develop new potential therapeutic targets for GSCs in GBM.

A novel role for Teneurin C-terminal Associated Peptide (TCAP) in the regulation of cardiac activity in the Sydney rock oyster, Saccostrea glomerata

Teneurin C-terminal associated peptide (TCAP) is an ancient bioactive peptide that is highly conserved in metazoans. TCAP administration reduces cellular and behavioural stress in vertebrate and urochordate models, yet despite numerous studies in higher animals, there is limited knowledge of its role in invertebrates. In particular, there are no studies on TCAP's effects on the heart of any metazoan, which is a critical organ in the stress response. We used the Sydney rock oyster (SRO) as an invertebrate model to investigate a potential role for sroTCAP in regulating cardiac activity, including during stress. sroTCAP is localized to the neural innervation network of the SRO heart, and suggested binding with various heart proteins related to metabolism and stress, including SOD, GAPDH and metabotropic glutamate receptor. Intramuscular injection of sroTCAP (10 pmol) significantly altered the expression of heart genes that are known to regulate remodelling processes under different conditions, and modulated several gene families responsible for stress mitigation. sroTCAP (1 and 10 pmol) was shown to cause transient bradycardia (heart rate was reduced by up to 63% and for up to 40 min post-administration), indicative of an unstressed state. In summary, this study has established a role for a TCAP in the regulation of cardiac activity through modulation of physiological and molecular components associated with energy conservation, stress and adaptation. This represents a novel function for TCAP and may have implications for higher-order metazoans.

Engineering of global transcription factors in Bacillus, a genetic tool for increasing product yields: a bioprocess overview

Transcriptional factors are well studied in bacteria for their global interactions and the effects they produce at the phenotypic level. Particularly, Bacillus subtilis has been widely employed as a model Gram-positive microorganism used to characterize these network interactions. Bacillus species are currently used as efficient commercial microbial platforms to produce diverse metabolites such as extracellular enzymes, antibiotics, surfactants, industrial chemicals, heterologous proteins, among others. However, the pleiotropic effects caused by the genetic modification of specific genes that codify for global regulators (transcription factors) have not been implicated commonly from a bioprocess point of view. Recently, these strategies have attracted the attention in Bacillus species because they can have an application to increase production efficiency of certain commercial interest metabolites. In this review, we update the recent advances that involve this trend in the use of genetic engineering (mutations, deletion, or overexpression) performed to global regulators such as Spo0A, CcpA, CodY and AbrB, which can provide an advantage for the development or improvement of bioprocesses that involve Bacillus species as production platforms. Genetic networks, regulation pathways and their relationship to the development of growth stages are also discussed to correlate the interactions that occur between these regulators, which are important to consider for application in the improvement of commercial-interest metabolites. Reported yields from these products currently produced mostly under laboratory conditions and, in a lesser extent at bioreactor level, are also discussed to give valuable perspectives about their potential use and developmental level directed to process optimization at large-scale.

scEpiLock: A Weakly Supervised Learning Framework for cis-Regulatory Element Localization and Variant Impact Quantification for Single-Cell Epigenetic Data

Recent advances in single-cell transposase-accessible chromatin using a sequencing assay (scATAC-seq) allow cellular heterogeneity dissection and regulatory landscape reconstruction with an unprecedented resolution. However, compared to bulk-sequencing, its ultra-high missingness remarkably reduces usable reads in each cell type, resulting in broader, fuzzier peak boundary definitions and limiting our ability to pinpoint functional regions and interpret variant impacts precisely. We propose a weakly supervised learning method, scEpiLock, to directly identify core functional regions from coarse peak labels and quantify variant impacts in a cell-type-specific manner. First, scEpiLock uses a multi-label classifier to predict chromatin accessibility via a deep convolutional neural network. Then, its weakly supervised object detection module further refines the peak boundary definition using gradient-weighted class activation mapping (Grad-CAM). Finally, scEpiLock provides cell-type-specific variant impacts within a given peak region. We applied scEpiLock to various scATAC-seq datasets and found that it achieves an area under receiver operating characteristic curve (AUC) of ~0.9 and an area under precision recall (AUPR) above 0.7. Besides, scEpiLock’s object detection condenses coarse peaks to only ⅓ of their original size while still reporting higher conservation scores. In addition, we applied scEpiLock on brain scATAC-seq data and reported several genome-wide association studies (GWAS) variants disrupting regulatory elements around known risk genes for Alzheimer’s disease, demonstrating its potential to provide cell-type-specific biological insights in disease studies.

DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)–to–gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET’s predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation–specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.

Bottom-Up Approach to the Discovery of Clinically Relevant Biomarker Genes: The Case of Colorectal Cancer

Traditional approaches to genome-wide marker discovery often follow a common top-down strategy, where a large scale ‘omics’ investigation is followed by the analysis of functional pathways involved, to narrow down the list of identified putative biomarkers, and to deconvolute gene expression networks, or to obtain an insight into genetic alterations observed in cancer. We set out to investigate whether a reverse approach would allow full or partial reconstruction of the transcriptional programs and biological pathways specific to a given cancer and whether the full or substantially expanded list of putative markers could thus be identified by starting with the partial knowledge of a few disease-specific markers. To this end, we used 10 well-documented differentially expressed markers of colorectal cancer (CRC), analyzed their transcription factor networks and biological pathways, and predicted the existence of 193 new putative markers. Incredibly, the use of a validation marker set of 10 other completely different known CRC markers and the same procedure resulted in a very similar set of 143 predicted markers. Of these, 138 were identical to those found using the training set, confirming our main hypothesis that a much-expanded set of disease markers can be predicted by starting with just a small subset of validated markers. Further to this, we validated the expression of 42 out of 138 top-ranked predicted markers experimentally using qPCR in surgically removed CRC tissues. We showed that 41 out of 42 mRNAs tested have significantly altered levels of mRNA expression in surgically excised CRC tissues. Of the markers tested, 36 have been reported to be associated with aspects of CRC in the past, whilst only limited published evidence exists for another three genes (BCL2, PDGFRB and TSC2), and no published evidence directly linking genes to CRC was found for CCNA1, SHC1 and TGFB3. Whilst we used CRC to test and validate our marker discovery strategy, the reported procedures apply more generally to cancer marker discovery.

Nucleus-directed fluorescent reporter system for promoter studies in the ectomycorrhizal fungus Laccaria bicolor

Currently ectomycorrhizal research suffers from a lack of molecular tools specifically adapted to study gene expression in fungal symbionts. Considering that, we designed pReNuK, a cloning vector for transcriptional promoter studies in the ectomycorrhizal basidiomycete Laccaria bicolor. The pReNuK vector offers the use of a nuclear localizing and chromatin incorporating histone H2B-mCherry fluorescent reporter protein and it is specifically optimized for efficient transgene expression in Laccaria. Moreover, pReNuK is designed to work in concert with Agrobacterium-mediated transformation under hygromycin B resistance selection. The functionality of the pReNuK reporter system was tested with the constitutive Laccaria glyceraldehyde 3-phosphate dehydrogenase gene promoter and further validated with the nitrogen source regulated nitrate reductase gene promoter. The expression of the nucleus-directed H2B-mCherry reporter is highly stable in time. Moreover, the transformation of Laccaria with pReNuK and the expression of the reporter do not have negative effects on the growth of the fungus. The pReNuK offers a novel tool for studying in vivo gene expression regulation in Laccaria, the leading fungal model for ectomycorrhizal research.

A two-microRNA signature predicts the progression of male thyroid cancer

In various cancers, microRNAs (miRNAs) are abnormally expressed, including thyroid cancer (TC). In recent years, the incidence of TC has increased annually around the world. Compared with female patients, male TC patients are more likely to have a postoperative recurrence and lymph node metastasis, and hence need second treatments. However, the molecular biological processes underlying this phenomenon are not understood. Therefore, we collected data on miRNA expression and clinical information of male TC patients from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs were identified between male TC tissues and matched normal tissues. The Kaplan–Meier method, univariate and multivariate Cox regressions, and receiver operating characteristic curve analyses were performed to assess the association between miRNAs and the disease-free survival of male TC patients. Gene Ontology (GO) and the Kyoto Encyclopaedia of Gene and Genome (KEGG) enrichment analyses were then used to explore the function of miRNA target genes. Furthermore, we evaluated the ability of the miRNA biomarker to predict survival in female TC patients. As a result, a total of 118 differentially expressed miRNAs were identified, including 25 upregulated and 93 downregulated miRNAs. Among them, miR-451a and miR-16-1-3p were confirmed to be independent prognostic factors for the disease-free survival rate. The target genes of miR-451a and miR-16-1-3p were identified, and functional analysis showed that these genes were enriched in 25 Go and KEGG accessions, including cell signal transduction, motor adhesion, phagocytosis, regulation of transcription, cell proliferation, angiogenesis, etc. Neither miR-451a and miR-16-1-3p, nor a prediction model based on both miRNAs effectively predicted survival in female TC patients. In conclusion, both miR-451a and miR-16-1-3p may play important roles in the processes of male TC. The two-miRNA signature involving miR-1258 and miR-193a may serve as a novel prognostic biomarker for male TC patients.

High-Throughput In Vitro Gene Expression Profile to Screen of Natural Herbals for Breast Cancer Treatment

Breast cancer has surpassed lung cancer as the most commonly diagnosed cancer in women worldwide. Some therapeutic drugs and approaches could cause side effects and weaken the immune system. The combination of conventional therapies and traditional Chinese medicine (TCM) significantly improves treatment efficacy in breast cancer. However, the chemical composition and underlying anti-tumor mechanisms of TCM still need to be investigated. The primary aim of this study is to provide unique insights to screen the natural components for breast cancer therapy using high-throughput transcriptome analysis. Differentially expressed genes were identified based on two conditions: single samples and groups were classified according to their pharmaceutical effect. Subsequently, the sample treated with E. cochinchinensis Lour. generated the most significant DEGs set, including 1,459 DEGs, 805 upregulated and 654 downregulated. Similarly, group 3 treatment contained the most DEGs (414 DEGs, 311 upregulated and 103 downregulated). KEGG pathway analyses showed five significant pathways associated with the inflammatory and metastasis processes in cancer, which include the TNF, IL−17, NF-kappa B, MAPK signaling pathways, and transcriptional misregulation in cancer. Samples were classified into 13 groups based on their pharmaceutical effects. The results of the KEGG pathway analyses remained consistent with signal samples; group 3 presents a high significance. A total of 21 genes were significantly regulated in these five pathways, interestingly, IL6, TNFAIP3, and BRIC3 were enriched on at least two pathways, seven genes (FOSL1, S100A9, CXCL12, ID2, PRS6KA3, AREG, and DUSP6) have been reported as the target biomarkers and even the diagnostic tools in cancer therapy. In addition, weighted correlation network analysis (WGCNA) was used to identify 18 modules. Among them, blue and thistle2 were the most relevant modules. A total of 26 hub genes in blue and thistle2 modules were identified as the hub genes. In conclusion, we screened out three new TCM (R. communis L., E. cochinchinensis Lour., and B. fruticosa) that have the potential to develop natural drugs for breast cancer therapy, and obtained the therapeutic targets.

Cancer and pH Dynamics: Transcriptional Regulation, Proteostasis, and the Need for New Molecular Tools

An emerging hallmark of cancer cells is dysregulated pH dynamics. Recent work has suggested that dysregulated intracellular pH (pHi) dynamics enable diverse cancer cellular behaviors at the population level, including cell proliferation, cell migration and metastasis, evasion of apoptosis, and metabolic adaptation. However, the molecular mechanisms driving pH-dependent cancer-associated cell behaviors are largely unknown. In this review article, we explore recent literature suggesting pHi dynamics may play a causative role in regulating or reinforcing tumorigenic transcriptional and proteostatic changes at the molecular level, and discuss outcomes on tumorigenesis and tumor heterogeneity. Most of the data we discuss are population-level analyses; lack of single-cell data is driven by a lack of tools to experimentally change pHi with spatiotemporal control. Data is also sparse on how pHi dynamics play out in complex in vivo microenvironments. To address this need, at the end of this review, we cover recent advances for live-cell pHi measurement at single-cell resolution. We also discuss the essential role for tool development in revealing mechanisms by which pHi dynamics drive tumor initiation, progression, and metastasis.

Expression of Genes Related to Carrageenan Synthesis during Carposporogenesis of the Red Seaweed Grateloupia imbricata

Carrageenan, the foremost constituent of extracellular matrix of some rhodophyta, is a galactan backbone with a different number of sulphate groups attached. Variations of degree of sulphation are associated with different types of carrageenans, which vary according to seaweed life cycles, and have consequences for the exploitation of this raw material. In this work, we used three well-recognised stages of development thalli and two stages of cystocarp maturation to analyse genes that encode addition and elimination of sulphate groups to cell-wall galactan of the red seaweed Grateloupia imbricata. Expressions of carbohydrate sulfotransferase and galactose-6 sulfurylase and genes encoding stress proteins such as cytochrome P450 and WD40, were examined. Results showed that transcript expression of carbohydrate sulfotransferase occurs at all stage of thalli development. Meanwhile galactose-6 sulfurylase expressions displayed different roles, which could be related to a temporal regulation of cystocarp maturation. Cytochrome P450 and WD40 are related to the disclosure and maturation of cystocarps of G. imbricata. Our conclusion is that differential expression of genes encoding proteins involved in the sulphation and desulphation of galactan backbone is associated with alterations in thalli development and cystocarp maturation in the red seaweed Grateloupia imbricata. Exploitation of industry-valued carrageenan will depend on insight into gene mechanisms of red seaweeds.

Ontologically simple theories do not indicate the true nature of complex biological systems: three test cases

A longstanding philosophical premise perceives simplicity as a desirable attribute of scientific theories. One of several raised justifications for this notion is that simple theories are more likely to indicate the true makeup of natural systems. Qualitatively parsimonious hypotheses and theories keep to a minimum the number of different postulated entities within a system. Formulation of such ontologically simple working hypotheses proved to be useful in the experimental probing of narrowly defined bio systems. It is less certain, however, whether qualitatively parsimonious theories are effective indicators of the true nature of complex biological systems. This paper assesses the success of ontologically simple theories in envisaging the makeup of three complex systems in bacteriology, immunology, and molecular biology. Evidence shows that parsimonious theories completely misconstrued the actual ontologically complex constitutions of the three examined systems. Since evolution and selective pressures typically produce ontologically intricate rather than simple bio systems, qualitatively parsimonious theories are mostly inapt indicators of the true nature of complex biological systems.