Metabolic memory underlying minimal residual disease in breast cancer

Structural variant and nucleosome occupancy dynamics postchemotherapy in a HER2+ breast cancer organoid model

The most common chemotherapeutics induce DNA damage to eradicate cancer cells, yet defective DNA repair can propagate mutations, instigating therapy resistance and secondary malignancies. Structural variants (SVs), arising from copy-number-imbalanced and -balanced DNA rearrangements, are a major driver of tumor evolution, yet understudied posttherapy. Here, we adapted single-cell template-strand sequencing (Strand-seq) to a HER2+ breast cancer model to investigate the formation of doxorubicin-induced de novo SVs. We coupled this approach with nucleosome occupancy (NO) measurements obtained from the same single cell to enable simultaneous SV detection and cell-type classification. Using organoids from TetO-CMYC/TetO-Neu/MMTV-rtTA mice modeling HER2+ breast cancer, we generated 459 Strand-seq libraries spanning various tumorigenesis stages, identifying a 7.4-fold increase in large chromosomal alterations post-doxorubicin. Complex DNA rearrangements, deletions, and duplications were prevalent across basal, luminal progenitor (LP), and mature luminal (ML) cells, indicating uniform susceptibility of these cell types to SV formation. Doxorubicin further elevated sister chromatid exchanges (SCEs), indicative of genomic stress persisting posttreatment. Altered nucleosome occupancy levels on distinct cancer-related genes further underscore the broad genomic impact of doxorubicin. The organoid-based system for single-cell multiomics established in this study paves the way for unraveling the most important therapy-associated SV mutational signatures, enabling systematic studies of the effect of therapy on cancer evolution.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Metabolic memory: mechanisms and diseases

Metabolic diseases and their complications impose health and economic burdens worldwide. Evidence from past experimental studies and clinical trials suggests our body may have the ability to remember the past metabolic environment, such as hyperglycemia or hyperlipidemia, thus leading to chronic inflammatory disorders and other diseases even after the elimination of these metabolic environments. The long-term effects of that aberrant metabolism on the body have been summarized as metabolic memory and are found to assume a crucial role in states of health and disease. Multiple molecular mechanisms collectively participate in metabolic memory management, resulting in different cellular alterations as well as tissue and organ dysfunctions, culminating in disease progression and even affecting offspring. The elucidation and expansion of the concept of metabolic memory provides more comprehensive insight into pathogenic mechanisms underlying metabolic diseases and complications and promises to be a new target in disease detection and management. Here, we retrace the history of relevant research on metabolic memory and summarize its salient characteristics. We provide a detailed discussion of the mechanisms by which metabolic memory may be involved in disease development at molecular, cellular, and organ levels, with emphasis on the impact of epigenetic modulations. Finally, we present some of the pivotal findings arguing in favor of targeting metabolic memory to develop therapeutic strategies for metabolic diseases and provide the latest reflections on the consequences of metabolic memory as well as their implications for human health and diseases.

Non-contact optical spectroscopy for tumor-sensitive diffuse reflectance and fluorescence measurements on murine subcutaneous tissue models: Monte Carlo modeling and experimental validations

Fiber-optic probes are commonly used in biomedical optical spectroscopy platforms for light delivery and collection. At the same time, it was reported that the inconsistent probe-sample contact could induce significant distortions in measured optical signals, which consequently cause large analysis errors. To address this challenge, non-contact optical spectroscopy has been explored for tissue characterizations. However, existing non-contact optical spectroscopy platforms primarily focused on diffuse reflectance measurements and may still use a fiber probe in which the probe was imaged onto the tissue surface using a lens, which serves as a non-contact probe for the measurements. Here, we report a fiber-probe-free, dark-field-based, non-contact optical spectroscopy for both diffuse reflectance and fluorescence measurements on turbid medium and tissues. To optimize the system design, we developed a novel Monte Carlo method to simulate such a non-contact setup for both diffuse reflectance and fluorescence measurements on murine subcutaneous tissue models with a spherical tumor-like target. We performed Monte Carlo simulations to identify the most tumor-sensitive configurations, from which we found that both the depth of the light focal point in tissue and the lens numerical aperture would dramatically affect the system's tumor detection sensitivity. We then conducted tissue-mimicking phantom studies to solidify these findings. Our reported Monte Carlo technique can be a useful computational tool for designing non-contact optical spectroscopy systems. Our non-contact optical setup and experimental findings will potentially offer a new approach for sensitive optical monitoring of tumor physiology in biological models using a non-contact optical spectroscopy platform to advance cancer research.

A new metabolic model of Drosophila melanogaster and the integrative analysis of Parkinson's disease

High conservation of the disease-associated genes between flies and humans facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.

Imagine beyond: recent breakthroughs and next challenges in mammary gland biology and breast cancer research

On 8 December 2022 the organizing committee of the European Network for Breast Development and Cancer labs (ENBDC) held its fifth annual Think Tank meeting in Amsterdam, the Netherlands. Here, we embraced the opportunity to look back to identify the most prominent breakthroughs of the past ten years and to reflect on the main challenges that lie ahead for our field in the years to come. The outcomes of these discussions are presented in this position paper, in the hope that it will serve as a summary of the current state of affairs in mammary gland biology and breast cancer research for early career researchers and other newcomers in the field, and as inspiration for scientists and clinicians to move the field forward.

Advances in the Diagnosis and Prognosis of Minimal Residual Lesions of Breast Cancer

PURPOSE

To review the latest research of minimal residual disease (MRD) in breast cancer as well as some emerging or potential detection methods for MRD in breast cancer.

METHODS

Springer, Wiley, and PubMed databases were searched for the electronic literature with search terms of breast cancer, minimal residual disease, circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), exosomes, etc. RESULTS: Minimal residual disease refers to the occult micrometastasis or minimal residual lesions detected in patients with tumor after radical treatment. An early and dynamic monitoring of breast cancer MRD can contribute to clinical treatment decision-making, improving the diagnosis accuracy and prognosis of breast cancer patients. The updated knowledge regarding MRD in breast cancer diagnosis and prognosis were summarized, followed by the review of several emerging or potential detection technologies for MRD in breast cancer. With the developed new MRD detection technologies referring to CTCs, ctDNA and exosomes, the role of MRD in breast cancer has been growingly verified, which is expected to serve as a new risk stratification factor and prognostic indicator for breast cancer.

CONCLUSION

This paper systematically reviews the research progress, opportunities and challenges in MRD in breast cancer in recent years.

Unravelling metabolic cross-feeding in a yeast-bacteria community using 13 C-based proteomics

Cross-feeding is fundamental to the diversity and function of microbial communities. However, identification of cross-fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use ¹³ C isotope tracing in peptides to elucidate cross-fed metabolites in co-cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with ¹³ C. Data analysis allowing for the possible mass-shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross-fed metabolites was further supported by metabolite labelling in the co-culture supernatant, and by diminished fitness of a galactose-negative yeast mutant in the community. Together, our results demonstrate the utility of ¹³ C-based proteomics for uncovering microbial interactions.

p53 inhibits the Urea cycle and represses polyamine biosynthesis in glioma cell lines

Glioma is the most common malignant tumor of the central nervous system. The urea cycle (UC) is an essential pathway to convert excess nitrogen and ammonia into the less toxic urea in humans. However, less is known about the functional significance of the urea cycle in glioma. p53 functions as a tumor suppressor and modulates several cellular functions and disease processes. In the present study, we aimed to explore whether p53 influences glioma progression by regulating the urea cycle. Here, we demonstrated the inhibitory impact of p53 on the expression of urea cycle enzymes and urea genesis in glioma cells. The level of polyamine, a urea cycle metabolite, was also regulated by p53 in glioma cells. Carbamoyl phosphate synthetase-1 (CPS1) is the first key enzyme involved in the urea cycle. Functionally, we demonstrated that CPS1 knockdown suppressed glioma cell proliferation, migration and invasion. Mechanistically, we demonstrated that the expression of ornithine decarboxylase (ODC), which determines the generation of polyamine, was regulated by CPS1. In addition, the impacts of p53 knockdown on ODC expression, glioma cell growth and aggressive phenotypes were significantly reversed by CPS1 inhibition. In conclusion, these results demonstrated that p53 inhibits polyamine metabolism by suppressing the urea cycle, which inhibits glioma progression.

Correlation between the Warburg effect and progression of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is ineligible for hormonal therapy and Her-2-targeted therapy due to the negative expression of the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2. Although targeted therapy and immunotherapy have been shown to attenuate the aggressiveness of TNBC partially, few patients have benefited from them. The conventional treatment for TNBC remains chemotherapy. Chemoresistance, however, impedes therapeutic progress over time, and chemotherapy toxicity increases the burden of cancer on patients. Therefore, introducing more advantageous TNBC treatment options is a necessity. Metabolic reprogramming centered on glucose metabolism is considered a hallmark of tumors. It is described as tumor cells tend to convert glucose to lactate even under normoxic conditions, a phenomenon known as the Warburg effect. Similar to Darwinian evolution, its emergence is attributed to the selective pressures formed by the hypoxic microenvironment of pre-malignant lesions. Of note, the Warburg effect does not disappear with changes in the microenvironment after the formation of malignant tumor phenotypes. Instead, it forms a constitutive expression mediated by mutations or epigenetic modifications, providing a robust selective survival advantage for primary and metastatic lesions. Expanding evidence has demonstrated that the Warburg effect mediates multiple invasive behaviors in TNBC, including proliferation, metastasis, recurrence, immune escape, and multidrug resistance. Moreover, the Warburg effect-targeted therapy has been testified to be feasible in inhibiting TNBC progression. However, not all TNBCs are sensitive to glycolysis inhibitors because TNBC cells flexibly switch their metabolic patterns to cope with different survival pressures, namely metabolic plasticity. Between the Warburg effect-targeted medicines and the actual curative effect, metabolic plasticity creates a divide that must be continuously researched and bridged.

Metabolomics-based mass spectrometry methods to analyze the chemical content of 3D organoid models

Metabolomics, the study of metabolites present in biological samples, can provide a global view of sample state as well as insights into biological changes caused by disease or environmental interactions. Mass spectrometry (MS) is commonly used for metabolomics analysis given its high-throughput capabilities, high sensitivity, and capacity to identify multiple compounds in complex samples simultaneously. MS can be coupled to separation methods that can handle small volumes, making it well suited for analyzing the metabolome of organoids, miniaturized three-dimensional aggregates of stem cells that model in vivo organs. Organoids are being used in research efforts to study human disease and development, and in the design of personalized drug treatments. For organoid models to be useful, they need to recapitulate morphological and chemical aspects, such as the metabolome, of the parent tissue. This review highlights the separation- and imaging-based MS-based metabolomics methods that have been used to analyze the chemical contents of organoids. Future perspectives on how MS techniques can be optimized to determine the accuracy of organoid models and expand the field of organoid research are also discussed.

Data integration and mechanistic modelling for breast cancer biology: Current state and future directions

Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of ‘big data’ and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.

Multi-omics insights into host-viral response and pathogenesis in Crimean-Congo hemorrhagic fever viruses for novel therapeutic target

The pathogenesis and host-viral interactions of the Crimean–Congo hemorrhagic fever orthonairovirus (CCHFV) are convoluted and not well evaluated. Application of the multi-omics system biology approaches, including biological network analysis in elucidating the complex host-viral response, interrogates the viral pathogenesis. The present study aimed to fingerprint the system-level alterations during acute CCHFV-infection and the cellular immune responses during productive CCHFV-replication in vitro. We used system-wide network-based system biology analysis of peripheral blood mononuclear cells (PBMCs) from a longitudinal cohort of CCHF patients during the acute phase of infection and after one year of recovery (convalescent phase) followed by untargeted quantitative proteomics analysis of the most permissive CCHFV-infected Huh7 and SW13 cells. In the RNAseq analysis of the PBMCs, comparing the acute and convalescent-phase, we observed system-level host’s metabolic reprogramming towards central carbon and energy metabolism (CCEM) with distinct upregulation of oxidative phosphorylation (OXPHOS) during CCHFV-infection. Upon application of network-based system biology methods, negative coordination of the biological signaling systems like FOXO/Notch axis and Akt/mTOR/HIF-1 signaling with metabolic pathways during CCHFV-infection were observed. The temporal quantitative proteomics in Huh7 showed a dynamic change in the CCEM over time and concordant with the cross-sectional proteomics in SW13 cells. By blocking the two key CCEM pathways, glycolysis and glutaminolysis, viral replication was inhibited in vitro. Activation of key interferon stimulating genes during infection suggested the role of type I and II interferon-mediated antiviral mechanisms both at the system level and during progressive replication.

Crimean-Congo hemorrhagic fever (CCHF) is an emerging disease that is increasingly spreading to new populations. The condition is now endemic in almost 30 countries in sub-Saharan Africa, South-Eastern Europe, the Middle East and Central Asia. CCHF is caused by a tick-borne virus and can cause uncontrolled bleeding. It has a mortality rate of up to 40%, and there are currently no vaccines or effective treatments available.

All viruses depend entirely on their hosts for reproduction, and they achieve this through hijacking the molecular machinery of the cells they infect. However, little is known about how the CCHF virus does this and how the cells respond. To understand more about the relationship between the cell’s metabolism and viral replication, Neogi, Elaldi et al. studied immune cells taken from patients during an infection and one year later.

The gene activity of the cells showed that the virus prefers to hijack processes known as central carbon and energy metabolism. These are the main regulator of the cellular energy supply and the production of essential chemicals. By using cancer drugs to block these key pathways, Neogi, Elaldi et al. could reduce the viral reproduction in laboratory cells.

These findings provide a clearer understanding of how the CCHF virus replicates inside human cells. By interfering with these processes, researchers could develop new antiviral strategies to treat the disease. One of the cancer drugs tested in cells, 2-DG, has been approved for emergency use against COVID-19 in some countries. Neogi, Elaldi et al. are now studying this further in animals with the hope of reaching clinical trials in the future.