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Background: Northern ecosystems are strongly influenced by herbivores that differ in their impacts on the ecosystem. Yet the role of herbivore diversity in shaping the structure and functioning of tundra ecosystems has been overlooked. With climate and land-use changes causing rapid shifts in Arctic species assemblages, a better understanding of the consequences of herbivore diversity changes for tundra ecosystem functioning is urgently needed. This systematic review synthesizes available evidence on the effects of herbivore diversity on different processes, functions, and properties of tundra ecosystems., Methods: Following a published protocol, our systematic review combined primary field studies retrieved from bibliographic databases, search engines and specialist websites that compared tundra ecosystem responses to different levels of vertebrate and invertebrate herbivore diversity. We used the number of functional groups of herbivores (i.e., functional group richness) as a measure of the diversity of the herbivore assemblage. We screened titles, abstracts, and full texts of studies using pre-defined eligibility criteria. We critically appraised the validity of the studies, tested the influence of different moderators, and conducted sensitivity analyses. Quantitative synthesis (i.e., calculation of effect sizes) was performed for ecosystem responses reported by at least five articles and meta-regressions including the effects of potential modifiers for those reported by at least 10 articles., Review Findings: The literature searches retrieved 5944 articles. After screening titles, abstracts, and full texts, 201 articles including 3713 studies (i.e., individual comparisons) were deemed relevant for the systematic review, with 2844 of these studies included in quantitative syntheses. The available evidence base on the effects of herbivore diversity on tundra ecosystems is concentrated around well-established research locations and focuses mainly on the impacts of vertebrate herbivores on vegetation. Overall, greater herbivore diversity led to increased abundance of feeding marks by herbivores and soil temperature, and to reduced total abundance of plants, graminoids, forbs, and litter, plant leaf size, plant height, and moss depth, but the effects of herbivore diversity were difficult to tease apart from those of excluding vertebrate herbivores. The effects of different functional groups of herbivores on graminoid and lichen abundance compensated each other, leading to no net effects when herbivore effects were combined. In turn, smaller herbivores and large-bodied herbivores only reduced plant height when occurring together but not when occurring separately. Greater herbivore diversity increased plant diversity in graminoid tundra but not in other habitat types., Conclusions: This systematic review underscores the importance of herbivore diversity in shaping the structure and function of Arctic ecosystems, with different functional groups of herbivores exerting additive or compensatory effects that can be modulated by environmental conditions. Still, many challenges remain to fully understand the complex impacts of herbivore diversity on tundra ecosystems. Future studies should explicitly address the role of herbivore diversity beyond presence-absence, targeting a broader range of ecosystem responses and explicitly including invertebrate herbivores. A better understanding of the role of herbivore diversity will enhance our ability to predict whether and where shifts in herbivore assemblages might mitigate or further amplify the impacts of environmental change on Arctic ecosystems., (© 2024. The Author(s).)

Opinion Statement: Prostate cancer (PCa) is the second most diagnosed malignant neoplasm and is one of the leading causes of cancer-related death in men worldwide. Despite significant advances in screening and treatment of PCa, given the heterogeneity of this disease, optimal personalized therapeutic strategies remain limited. However, emerging predictive and prognostic biomarkers based on individual patient profiles in combination with computer-assisted diagnostics have the potential to guide precision medicine, where patients may benefit from therapeutic approaches optimally suited to their disease. Also, the integration of genotypic and phenotypic diagnostic methods is supporting better informed treatment decisions. Focusing on advanced PCa, this review discusses polygenic risk scores for screening of PCa and common genomic aberrations in androgen receptor (AR), PTEN-PI3K-AKT, and DNA damage response (DDR) pathways, considering clinical implications for diagnosis, prognosis, and treatment prediction. Furthermore, we evaluate liquid biopsy, protein biomarkers such as serum testosterone levels, SLFN11 expression, total alkaline phosphatase (tALP), neutrophil-to-lymphocyte ratio (NLR), tissue biopsy, and advanced imaging tools, summarizing current phenotypic biomarkers and envisaging more effective utilization of diagnostic and prognostic biomarkers in advanced PCa. We conclude that prognostic and treatment predictive biomarker discovery can improve the management of patients, especially in metastatic stages of advanced PCa. This will result in decreased mortality and enhanced quality of life and help design a personalized treatment regimen., (© 2023. The Author(s).)

Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications., Competing Interests: F.V. declares a relationship with OmniOmics.AI Pty., Ltd., which may involve financial interests or other forms of cooperative ventures., (© 2023 The Author(s).)

Background: Metastatic cancer cells exploit Epithelial-mesenchymal-transition (EMT) to enhance their migration, invasion, and resistance to treatments. Recent studies highlight that elevated levels of copper are implicated in cancer progression and metastasis. Clinical trials using copper chelators are associated with improved patient survival; however, the molecular mechanisms by which copper depletion inhibits tumor progression and metastasis are poorly understood. This remains a major hurdle to the clinical translation of copper chelators. Here, we propose that copper chelation inhibits metastasis by reducing TGF-β levels and EMT signaling. Given that many drugs targeting TGF-β have failed in clinical trials, partly because of severe side effects arising in patients, we hypothesized that copper chelation therapy might be a less toxic alternative to target the TGF-β/EMT axis., Results: Our cytokine array and RNA-seq data suggested a link between copper homeostasis, TGF-β and EMT process. To validate this hypothesis, we performed single-cell imaging, protein assays, and in vivo studies. Here, we used the copper chelating agent TEPA to block copper trafficking. Our in vivo study showed a reduction of TGF-β levels and metastasis to the lung in the TNBC mouse model. Mechanistically, TEPA significantly downregulated canonical (TGF-β/SMAD2&3) and non-canonical (TGF-β/PI3K/AKT, TGF-β/RAS/RAF/MEK/ERK, and TGF-β/WNT/β-catenin) TGF-β signaling pathways. Additionally, EMT markers of MMP-9, MMP-14, Vimentin, β-catenin, ZEB1, and p-SMAD2 were downregulated, and EMT transcription factors of SNAI1, ZEB1, and p-SMAD2 accumulated in the cytoplasm after treatment., Conclusions: Our study suggests that copper chelation therapy represents a potentially effective therapeutic approach for targeting TGF-β and inhibiting EMT in a diverse range of cancers., (© 2023. Crown.)

Androgen receptor variant 7 (AR-V7) is an important biomarker to guide treatment options for castration-resistant prostate cancer (CRPC) patients. Its detectability in circulating tumour cells (CTCs) opens non-invasive diagnostic avenues. While detectable at the transcript level, AR-V7 protein detection in CTCs may add additional information and clinical relevance. The aim of this study was to compare commercially available anti-AR-V7 antibodies and establish reliable AR-V7 immunocytostaining applicable to CTCs from prostate cancer (PCa) patients. We compared seven AR-V7 antibodies by western blotting and immmunocytostaining using a set of PCa cell lines with known AR/AR-V7 status. The emerging best antibody was validated for detection of CRPC patient CTCs enriched by negative depletion of leucocytes. The anti-AR-V7 antibody, clone E308L emerged as the best antibody in regard to signal to noise ratio with a specific nuclear signal. Moreover, this antibody detects CRPC CTCs more efficiently compared to an antibody previously shown to detect AR-V7 CTCs. We have determined the best antibody for AR-V7 detection of CTCs, which will open future studies to correlate AR-V7 subcellular localization and potential co-localization with other proteins and cellular structures to patient outcomes., (© 2022. The Author(s).)

Abstract: Phenotypic plasticity describes the ability of cancer cells to undergo dynamic, nongenetic cell state changes that amplify cancer heterogeneity to promote metastasis and therapy evasion. Thus, cancer cells occupy a continuous spectrum of phenotypic states connected by trajectories defining dynamic transitions upon a cancer cell state landscape. With technologies proliferating to systematically record molecular mechanisms at single-cell resolution, we illuminate manifold learning techniques as emerging computational tools to effectively model cell state dynamics in a way that mimics our understanding of the cell state landscape. We anticipate that "state-gating" therapies targeting phenotypic plasticity will limit cancer heterogeneity, metastasis, and therapy resistance., Significance: Nongenetic mechanisms underlying phenotypic plasticity have emerged as significant drivers of tumor heterogeneity, metastasis, and therapy resistance. Herein, we discuss new experimental and computational techniques to define phenotypic plasticity as a scaffold to guide accelerated progress in uncovering new vulnerabilities for therapeutic exploitation., (©2022 The Authors; Published by the American Association for Cancer Research.)