Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Standard chemotherapy impacts on in vitro cellular heterogeneity in spheroids enriched with cancer stem cells (CSCs) derived from triple-negative breast cancer cell line.

Triple-negative breast cancer is a heterogeneous disease with high recurrence and mortality, linked to cancer stem cells (CSCs). Our study characterized distinct cell subpopulations and signaling pathways to explore chemoresistance. We observed cellular heterogeneity among and within the cells regarding phenotyping and drug response. In untreated BT-549 cells, we noted plasticity properties in both CD44+/CD24+/CD146+ hybrid cells and CD44-/CD24+/CD146+ epithelial cells, enabling phenotypic conversion into CD44+/CD24-/CD146- epithelial-mesenchymal transition (EMT)-like like breast CSCs (BCSCs). Additionally, non-BCSCs may give rise to ALDH+ epithelial-like BCSCs. Enriched BCSCs demonstrated the potential to differentiation into CD44-/CD24-/CD146- cells and exhibited self-renewal capabilities. Similar phenotypic plasticity was not observed in untreated Hs 578T and HMT-3522 S1 cells. BT-549 cells were more resistant to paclitaxel/PTX than to doxorubicin/DOX, a phenomenon potentially linked to the presence of CD24+ cells prior to treatment. Under the CSCs-enriched spheroids model, BT-549 demonstrated extreme resistance to DOX, likely due to the enrichment of BCSCs CD44+/CD24-/CD146- and the tumor cells CD44-/CD24-/CD146-. Additionally, DOX treatment induced the enrichment of plastic and chemoresistant cells, further exacerbating resistance mechanisms. BT-549 exhibited high heterogeneity, leading to significant alterations in cell subpopulations under BCSCs enrichment, demonstrating increased phenotypic plasticity during EMT. This phenomenon appears to play a major role in DOX resistance, as indicated by the presence of the refractory cells CD44+/CD24-/CD146- BCSCs EMT-like, CD44-/CD24-/CD146- tumor cells, and elevated STAT3 expression. Gene expression data from BT-549 CSCs-enriched spheroids suggests that ferroptosis may be occurring via autophagic regulation triggered by RAB7A, highlighting this gene as a potential therapeutic target.

The prognostic value of [18F]FDG PET/CT texture analysis prior to transplantation for unresectable colorectal liver metastases.

INTRODUCTION: To determine whether heterogeneity in colorectal liver metastases (CRLM) 18F fluorodeoxyglucose [18F]FDG distribution is predictive of disease-free survival (DFS) and overall survival (OS) following liver transplantation (LT) for unresectable CRLM. METHODS: The preoperative [18F]FDG positron emission tomography/computed tomography examinations of all patients in the secondary cancer 1 and 2 studies were retrospectively assessed. Maximum standardized uptake value (SUVmax), metabolic tumour volume (MTV), and six texture heterogeneity parameters (joint entropyGLCM, dissimilarityGLCM, grey level varianceSZM, size zone varianceSZM, and zone percentageSZM, and morphological feature convex deficiency) were obtained. DFS and OS for patients over and under the median value for each of these parameters were compared by using the Kaplan Meier method and log rank test. RESULTS: Twenty-eight out of 40 patients who underwent LT for unresectable CRLM had liver metastases with uptake above liver background and were eligible for inclusion. Low MTV (p < 0.001) and dissimilarityGLCM (p = 0.016) were correlated to longer DFS. Low MTV (p < 0.001) and low values of the texture parameters dissimilarityGLCM (p = 0.038), joint entropyGLCM (p = 0.015) and zone percentageSZM (p = 0.037) were significantly correlated to longer OS. SUVmax was not correlated to DFS and OS. CONCLUSION: Although some texture parameters were able to significantly predict DFS and OS, MTV seems to be superior to predict both DFS and OS following LT for unresectable CRLM.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

Mimicking and analyzing the tumor microenvironment.

The tumor microenvironment (TME) is increasingly appreciated to play a decisive role in cancer development and response to therapy in all solid tumors. Hypoxia, acidosis, high interstitial pressure, nutrient-poor conditions, and high cellular heterogeneity of the TME arise from interactions between cancer cells and their environment. These properties, in turn, play key roles in the aggressiveness and therapy resistance of the disease, through complex reciprocal interactions between the cancer cell genotype and phenotype, and the physicochemical and cellular environment. Understanding this complexity requires the combination of sophisticated cancer models and high-resolution analysis tools. Models must allow both control and analysis of cellular and acellular TME properties, and analyses must be able to capture the complexity at high depth and spatial resolution. Here, we review the advantages and limitations of key models and methods in order to guide further TME research and outline future challenges.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues.

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

Recent progress and applications of single-cell sequencing technology in breast cancer.

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies.

Associations of Working From Home Frequency With Accelerometer-Measured Physical Activity and Sedentary Behavior in Japanese White-Collar Workers: A Cross-Sectional Analysis of the Meiji Yasuda LifeStyle Study.

BACKGROUND: The association of working from home (WFH) with physical activity (PA) and sedentary behavior (SB) has been explored; however, this association during periods without stringent measures to combat coronavirus disease 2019 is undercharacterized. Particularly, few studies have evaluated the potential effect modification on its associations. Therefore, this study aimed to investigate the associations of WFH frequency with accelerometer-measured PA and SB, along with its effect modifiers, among Japanese white-collar workers during the later pandemic period. METHODS: A cross-sectional study involving 1133 white-collar workers residing in the Tokyo metropolitan area was conducted between April 2022 and March 2023. WFH frequency was evaluated through a self-report questionnaire, while SB and PA during weekdays were measured using a triaxial accelerometer. Linear regression models adjusting for potential confounders were used to examine the association of WFH frequency with PA and SB. RESULTS: Regardless of activity intensity, individuals who worked at home, even for 1 to 2 days per week, demonstrated lower levels of PA and higher levels of SB as compared with those who did not (P < .05). For example, difference between never and ≥5 days per week WFH was approximately 70 minutes/day for total PA and about 4000 steps/day for step counts. Associations between WFH and decreased PA were more pronounced in workers who were older, female, less educated, in sales and service work, nonregular staff, and precontemplating changes in exercise and eating habits. CONCLUSIONS: We report that WFH was associated with decreased PA and increased SB in Japanese white-collar workers with pronounced associated in several subgroups.

Understanding the autophagic functions in cancer stem cell maintenance and therapy resistance.

Complex tumour ecosystem comprising tumour cells and its associated tumour microenvironment (TME) constantly influence the tumoural behaviour and ultimately impact therapy failure, disease progression, recurrence and poor overall survival of patients. Crosstalk between tumour cells and TME amplifies the complexity by creating metabolic changes such as hypoxic environment and nutrient fluctuations. These changes in TME initiate stem cell-like programmes in cancer cells, contribute to tumoural heterogeneity and increase tumour robustness. Recent studies demonstrate the multifaceted role of autophagy in promoting fibroblast production, stemness, cancer cell survival during longer periods of dormancy, eventual growth of metastatic disease and disease resistance. Recent ongoing studies examine autophagy/mitophagy as a powerful survival strategy in response to environmental stress including nutrient deprivation, hypoxia and environmental stress in TME. It prevents irreversible senescence, promotes dormant stem-like state, induces epithelial-mesenchymal transition and increases migratory and invasive potential of tumour cells. The present review discusses various theories and mechanisms behind the autophagy-dependent induction of cancer stem cell (CSC) phenotype. Given the role of autophagic functions in CSC aggressiveness and therapeutic resistance, various mechanisms and studies based on suppressing cellular plasticity by blocking autophagy as a powerful therapeutic strategy to kill tumour cells are discussed.

Treatment-induced stemness and lineage plasticity in driving prostate cancer therapy resistance.

Most human cancers are heterogeneous consisting of cancer cells at different epigenetic and transcriptional states and with distinct phenotypes, functions, and drug sensitivities. This inherent cancer cell heterogeneity contributes to tumor resistance to clinical treatment, especially the molecularly targeted therapies such as tyrosine kinase inhibitors (TKIs) and androgen receptor signaling inhibitors (ARSIs). Therapeutic interventions, in turn, induce lineage plasticity (also called lineage infidelity) in cancer cells that also drives therapy resistance. In this Perspective, we focus our discussions on cancer cell lineage plasticity manifested as treatment-induced switching of epithelial cancer cells to basal/stem-like, mesenchymal, and neural lineages. We employ prostate cancer (PCa) as the prime example to highlight ARSI-induced lineage plasticity during and towards development of castration-resistant PCa (CRPC). We further discuss how the tumor microenvironment (TME) influences therapy-induced lineage plasticity. Finally, we offer an updated summary on the regulators and mechanisms driving cancer cell lineage infidelity, which should be therapeutically targeted to extend the therapeutic window and improve patients' survival.

The evolutionary history of metastatic pancreatic neuroendocrine tumours reveals a therapy driven route to high-grade transformation.

Tumour evolution with acquisition of more aggressive disease characteristics is a hallmark of disseminated cancer. Metastatic pancreatic neuroendocrine tumours (PanNETs) in particular may progress from a low/intermediate to a high-grade disease. The aim of this work was to understand the molecular mechanisms underlying metastatic progression as well as PanNET transformation from a low/intermediate to a high-grade disease. We performed multi-omics analysis (genome/exome sequencing, total RNA-sequencing and methylation array) of 32 longitudinal samples from six patients with metastatic low/intermediate grade PanNET. The clonal composition of tumour lesions and underlying phylogeny of each patient were determined with bioinformatics analyses. Findings were validated in post-alkylating chemotherapy samples from 24 patients with PanNET using targeted next generation sequencing. We validate the current PanNET evolutionary model with MEN1 inactivation that occurs very early in tumourigenesis. This was followed by pronounced genetic diversity on both spatial and temporal levels, with parallel and convergent tumour evolution involving the ATRX/DAXX and mechanistic target of the rapamycin (mTOR) pathways. Following alkylating chemotherapy treatment, some PanNETs developed mismatch repair deficiency and acquired a hypermutational phenotype. This was validated among 16 patients with PanNET who had high-grade progression after alkylating chemotherapy, of whom eight had a tumour mutational burden >50 (50%). In comparison, among the eight patients who did not show high-grade progression, 0 had a tumour mutational burden >50 (0%; odds ratio 'infinite', 95% confidence interval 1.8 to 'infinite', p = 0.02). Our findings contribute to broaden the understanding of metastatic/high-grade PanNETs and suggests that therapy driven disease evolution is an important hallmark of this disease. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions.

BACKGROUND: With the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions. METHODS: CT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists' interpretation. RESULTS: Combining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P < 0.001) and small renal lesion (AUC = 0.935 vs 0.745, P < 0.001) test cohorts. Furthermore, the combined model exhibited favorable concordance and provided the highest net benefit across threshold probabilities exceeding 60%. In the whole and small renal lesion test cohorts, the AUCs for subgroups with predicted risk below or above 95% sensitivity and specificity cutoffs were 0.974 and 0.978, respectively. CONCLUSIONS: The combined model, incorporating intratumoral and peritumoral radiomic features, ecological diversity features, and clinical factors showed good performance for distinguishing benign from malignant renal lesions, surpassing radiologists' diagnoses in both whole and small renal lesions. It has the potential to save patients from unnecessary invasive biopsies/surgeries and to enhance clinical decision-making.

Targeting Mitochondria: Influence of Metabolites on Mitochondrial Heterogeneity.

Mitochondria are vital organelles that provide energy for the metabolic processes of cells. These include regulating cellular metabolism, autophagy, apoptosis, calcium ions, and signaling processes. Despite their varying functions, mitochondria are considered semi-independent organelles that possess their own genome, known as mtDNA, which encodes 13 proteins crucial for oxidative phosphorylation. However, their diversity reflects an organism's adaptation to physiological conditions and plays a complex function in cellular metabolism. Mitochondrial heterogeneity exists at the single-cell and tissue levels, impacting cell shape, size, membrane potential, and function. This heterogeneity can contribute to the progression of diseases such as neurodegenerative diseases, metabolic diseases, and cancer. Mitochondrial dynamics enhance the stability of cells and sufficient energy requirement, but these activities are not universal and can lead to uneven mitochondria, resulting in heterogeneity. Factors such as genetics, environmental compounds, and signaling pathways are found to affect these cellular processes and heterogeneity. Additionally, the varying roles of metabolites such as NADH and ATP affect glycolysis's speed and efficiency. An imbalance in metabolites can impair ATP production and redox potential in the mitochondria. Therefore, this review will explore the influence of metabolites in shaping mitochondrial morphology, how these changes contribute to age-related diseases and the therapeutic targets for regulating mitochondrial heterogeneity.

Enhancing the prediction of the invasiveness of pulmonary adenocarcinomas presenting as pure ground-glass nodules: Integrating intratumor heterogeneity score with clinical-radiological features via machine learning in a multicenter study.

Objective: The invasiveness of lung adenocarcinoma significantly impacts clinical decision-making. However, assessing this invasiveness preoperatively, especially when it manifests as pure ground-glass nodules (pGGN) on CT scans, poses challenges. This study aims to quantify intratumor heterogeneity (ITH) and determine whether the ITH score can enhance the accuracy of invasiveness predictions. Methods: A total of 524 patients with lung adenocarcinomas presenting as pGGN were enrolled in the study, with 177 (33.78%) receiving a pathologic diagnosis of invasiveness. Four diagnostic approaches were developed to predict the invasiveness of lung adenocarcinoma presenting as pGGN: (1) conventional lesion size, (2) ITH score, (3) clinical-radiological features (ClinRad), and (4) integration of the ITH score with ClinRad. ClinRad alone or in combination with the ITH score served as the input for 11 machine learning approaches. The trained models were evaluated in an independent validation cohort, and the area under the curve (AUC) was calculated to assess classification performance. Results: The conventional lesion size showed the lowest performance, with an AUC of 0.826 (95% confidence interval [CI]: 0.758-0.894), while the ITH score outperformed it with an AUC of 0.846 (95% CI: 0.787-0.905). The CatBoost model performed best when the ITH score and ClinRad were both used as input features, leading to the development of an ITH-ClinRad-guided CatBoost classifier. CatBoost also excelled with ClinRad alone, resulting in a ClinRad-guided CatBoost classifier with an AUC of 0.830 (95% CI: 0.764-0.896), surpassed by the ITH-ClinRad-guided CatBoost classifier with an AUC of 0.871 (95% CI: 0.818-0.924). Conclusion: The ITH-ClinRad-guided CatBoost classifier emerges as a promising tool with significant potential to revolutionize the management of lung adenocarcinomas presenting as pGGNs.

Natural Phycocyanin/Paclitaxel Micelle Delivery of Therapeutic P53 to Activate Apoptosis for HER2 or ER Positive Breast Cancer Therapy.

The P53 gene is commonly mutated in breast cancer, protein based the gene as anticancer drugs could provide efficient and stable advantages by restoring the function of the wild-type P53 protein. In this study, we describe the creation and utilization of a micelle composed by natural phycocyanin and paclitaxel and grafting anti-HER2 (PPH), which effectively packages and transports recombinant P53 protein with anti-ER (PE), resulting in a new entity designated as PE@PPH, to address localization obstacles and modify cellular tropism to the cell membrane or nucleus. The results indicate that PE@PPH has strong antitumor properties, even at low doses of PTX both in vitro and in vivo. These findings suggest that PE@PPH could be an enhancing micelle for delivering therapeutic proteins and promoting protein functional recovery, particularly in addressing the challenges posed by tumor heterogeneity in breast cancer.

Machine learning based intratumor heterogeneity signature for predicting prognosis and immunotherapy benefit in stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a prevalent malignancy that is highly aggressive and heterogeneous. Intratumor heterogeneity (ITH) showed strong link to tumor progression and metastasis. High ITH may promote tumor evolution. An ITH-related signature (IRS) was created using as integrative technique including 10 machine learning methods based on TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets. The relevance of IRS in predicting the advantages of immunotherapy was assessed using a number of prediction scores and three immunotherapy datasets (GSE78220, IMvigor210 and GSE91061). Vitro experiments were performed to verify the biological functions of AKR1B1. The RSF + Enet (alpha = 0.1) projected model was proposed as the ideal IRS because it had the highest average C-index. The IRS demonstrated a strong performance in serving as an independent risk factor for the clinical outcome of STAD patients. It performed exceptionally well in predicting the overall survival rate of STAD patients, as seen by the TCGA cohort's AUC of 1-, 3-, and 5-year ROC curves, which were 0.689, 0.683, and 0.669, respectively. A low IRS score demonstrated a superior response to immunotherapy, as seen by a lower TIDE score, lower immune escape score, greater TMB score, higher PD1&CTLA4 immunophenoscore, higher response rate, and improved prognosis. Common chemotherapeutic and targeted treatment regimens had lower IC50 values in the group with higher IRS scores. Vitro experiment showed that AKR1B1 was upregulated in STAD and knockdown of AKR1B1 obviously suppressed tumor cell proliferation and migration. The present investigation produced the best IRS for STAD, which may be applied to prognostication, risk stratification, and therapy planning for STAD patients.

Matching clinical and genetic data in pediatric patients at risk of developing cystic kidney disease.

BACKGROUND: Cystic kidney disease is a heterogeneous group of hereditary and non-hereditary pathologic conditions, associated with the development of renal cysts. These conditions may be present both in children and adults. Cysts can even be observed already during the prenatal age, and pediatric patients with cysts need to be clinically monitored. An early clinical and genetic diagnosis is therefore mandatory for optimal patient management. The aim of this study was to perform genetic analyses in patients with echographic evidence of kidney cysts to provide an early molecular diagnosis. METHODS: A cohort of 70 pediatric patients was enrolled and clinically studied at the time of first recruitment and at follow-up. Genetic testing by clinical exome sequencing was performed and a panel of genes responsible for "cystic kidneys" was analyzed to identify causative variants. Sanger validation and segregation studies were exploited for the final classification of the variants and accurate genetic counseling. RESULTS: Data showed that 53/70 of pediatric patients referred with a clinical suspicion of cystic kidney disease presented a causative genetic variant. In a significant proportion of the cohort (24/70), evidence of hyper-echogenic/cystic kidneys was already present in the prenatal period, even in the absence of a positive family history. CONCLUSIONS: This study suggests that cystic kidney disease may develop since the very early stages of life and that screening programs based on ultrasound scans and genetic testing play a critical role in diagnosis, allowing for better clinical management and tailored genetic counseling to the family.

Exploiting phenotypic heterogeneity to improve production of glutathione by yeast.

BACKGROUND: Gene expression noise (variation in gene expression among individual cells of a genetically uniform cell population) can result in heterogenous metabolite production by industrial microorganisms, with cultures containing both low- and high-producing cells. The presence of low-producing individuals may be a factor limiting the potential for high yields. This study tested the hypothesis that low-producing variants in yeast cell populations can be continuously counter-selected, to increase net production of glutathione (GSH) as an exemplar product. RESULTS: A counter-selection system was engineered in Saccharomyces cerevisiae based on the known feedback inhibition of gamma-glutamylcysteine synthetase (GSH1) gene expression, which is rate limiting for GSH synthesis: the GSH1 ORF and the counter-selectable marker GAP1 were expressed under control of the TEF1 and GSH-regulated GSH1 promoters, respectively. An 18% increase in the mean cellular GSH level was achieved in cultures of the engineered strain supplemented with D-histidine to counter-select cells with high GAP1 expression (i.e. low GSH-producing cells). The phenotype was non-heritable and did not arise from a generic response to D-histidine, unlike that with certain other test-constructs prepared with alternative markers. CONCLUSIONS: The results corroborate that the system developed here improves GSH production by targeting low-producing cells. This supports the potential for exploiting end-product/promoter interactions to enrich high-producing cells in phenotypically heterogeneous populations, in order to improve metabolite production by yeast.

Concomitant BRAF V600E and NRAS Q61R mutations in the same thyroid nodule: a case report.

Background: Papillary thyroid cancer (PTC) is the most common type of well-differentiated endocrine malignancy. Generally, thyroid nodules with multiple oncogenic mutations are uncommon with an occurrence which may be related to more aggressive biological behavior of tumors. RET/PTC rearrangement, RAS, and BRAF mutations are considered to be mutually exclusive in PTC. Concomitant RET/PTC, RAS, or BRAF mutations have been documented, although the impact of these mutations for tumor growth and survival is debated. Case Description: Here we present a rare case of woman 46 years old with a neck mass and thyroid nodule classified as TIR5 on cytological examination. We found contemporary BRAF p.(Val600Glu) [p.(V600E); c.1799T>A] and NRAS p.(Gln61Arg) [p.(Q61R); c.182A>G] mutations in morphologically different areas within the same lobe (the right one); The two lesions show different morphology. The mutated BRAF lesion showed morphological characteristics compatible with classic papillary carcinoma. The mutant NRAS lesion shows morphological features compatible with follicular variant papillary carcinoma. To the best of our knowledges, this is the first time that such mutations, which are normally mutually exclusive, have been detected at the same time. Conclusions: The finding of synchronous mutations is a rare occurrence suggesting for intratumoral heterogeneity (ITH) even in PTC. Patients with multiple mutations have a clinical worse prognosis, generally characterized by an aggressive thyroid cancer, which may influence the surgical treatment, chemotherapy, and BRAF V600E mutation-targeting therapy.