The African killifish: A short-lived vertebrate model to study the biology of sarcopenia and longevity.

Sarcopenia, the age-related decline in muscle function, places a considerable burden on health-care systems. While the stereotypic hallmarks of sarcopenia are well characterized, their contribution to muscle wasting remains elusive, which is partly due to the limited availability of animal models. Here, we have performed cellular and molecular characterization of skeletal muscle from the African killifish-an extremely short-lived vertebrate-revealing that while many characteristics deteriorate with increasing age, supporting the use of killifish as a model for sarcopenia research, some features surprisingly reverse to an "early-life" state in the extremely old stages. This suggests that in extremely old animals, there may be mechanisms that prevent further deterioration of skeletal muscle, contributing to an extension of life span. In line with this, we report a reduction in mortality rates in extremely old killifish. To identify mechanisms for this phenomenon, we used a systems metabolomics approach, which revealed that during aging there is a striking depletion of triglycerides, mimicking a state of calorie restriction. This results in the activation of mitohormesis, increasing Sirt1 levels, which improves lipid metabolism and maintains nutrient homeostasis in extremely old animals. Pharmacological induction of Sirt1 in aged animals was sufficient to induce a late life-like metabolic profile, supporting its role in life span extension in vertebrate populations that are naturally long-lived. Collectively, our results demonstrate that killifish are not only a novel model to study the biological processes that govern sarcopenia, but they also provide a unique vertebrate system to dissect the regulation of longevity.

Parallel use of human stem cell lung and heart models provide insights for SARS-CoV-2 treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily infects the respiratory tract, but pulmonary and cardiac complications occur in severe coronavirus disease 2019 (COVID-19). To elucidate molecular mechanisms in the lung and heart, we conducted paired experiments in human stem cell-derived lung alveolar type II (AT2) epithelial cell and cardiac cultures infected with SARS-CoV-2. With CRISPR-Cas9-mediated knockout of ACE2, we demonstrated that angiotensin-converting enzyme 2 (ACE2) was essential for SARS-CoV-2 infection of both cell types but that further processing in lung cells required TMPRSS2, while cardiac cells required the endosomal pathway. Host responses were significantly different; transcriptome profiling and phosphoproteomics responses depended strongly on the cell type. We identified several antiviral compounds with distinct antiviral and toxicity profiles in lung AT2 and cardiac cells, highlighting the importance of using several relevant cell types for evaluation of antiviral drugs. Our data provide new insights into rational drug combinations for effective treatment of a virus that affects multiple organ systems.

Clonal selection parallels between normal and cancer tissues.

Clonal selection and drift drive both normal tissue and cancer development. However, the biological mechanisms and environmental conditions underpinning these processes remain to be elucidated. Clonal selection models are centered in Darwinian evolutionary theory, where some clones with the fittest features are selected and populate the tissue or tumor. We suggest that different subclasses of stem cells, each of which is responsible for a distinct feature of the selection process, share common features between normal and cancer conditions. While active stem cells populate the tissue, dormant cells account for tissue replenishment/regeneration in both normal and cancerous tissues. We also discuss potential mechanisms that drive clonal drift, their interactions with clonal selection, and their similarities during normal and cancer tissue development.

Systematic molecular profiling of acute leukemia cancer stem cells allows identification of druggable targets.

Acute myeloid leukemia (AML) is one of the most prevalent and acute blood cancers with a poor prognosis and low overall survival rate, especially in the elderly. Although several new AML markers and drug targets have been recently identified, the rate of long-term cancer eradication has not improved significantly due to the presence and drug resistance of AML cancer stem cells (CSCs). Here we develop a novel computational pipeline to analyze the transcriptomic profiles of AML cancer (stem) cells and identify novel candidate AML CSC markers and drug targets. In our novel pipeline we apply a top-down meta-analysis strategy to integrate The Cancer Genome Atlas data with CSC datasets to infer cell stemness features. As a result, a set of genes termed the "AML key CSC genes" along with all the available drugs/compounds that could target them were identified. Overall, our novel computational pipeline could retrieve known cancer drugs (Carfilzomib) and predicted novel drugs such as Zonisamide, Amitriptyline, and their targets amongst the top ranked drugs and drug targets for targeting AML. Additionally, the pipeline applied in this study could be used for the identification of CSC-specific markers, drivers and their respective targeting drugs in other cancer types.

Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks.

Biological systems are composed of highly complex networks, and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points within a network. However, current methods do not address all the topological dimensions of a network or correct for inherent positional biases, which limits their applicability. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures and developed an algorithm termed Integrated Value of Influence (IVI), which integrates the most important and commonly used network centrality measures in an unbiased way. When compared against 12 other contemporary influential node identification methods on ten different networks, the IVI algorithm outperformed all other assessed methods. Using this versatile method, network researchers can now identify the most influential network nodes.

Carcinogenic effects of circadian disruption: an epigenetic viewpoint.

Circadian rhythms refer to the endogenous rhythms that are generated to synchronize physiology and behavior with 24-h environmental cues. These rhythms are regulated by both external cues and molecular clock mechanisms in almost all cells. Disruption of circadian rhythms, which is called circadian disruption, affects many biological processes within the body and results in different long-term diseases, including cancer. Circadian regulatory pathways result in rhythmic epigenetic modifications and the formation of circadian epigenomes. Aberrant epigenetic modifications, such as hypermethylation, due to circadian disruption may be involved in the transformation of normal cells into cancer cells. Several studies have indicated an epigenetic basis for the carcinogenic effects of circadian disruption. In this review, I first discuss some of the circadian genes and regulatory proteins. Then, I summarize the current evidence related to the epigenetic modifications that result in circadian disruption. In addition, I explain the carcinogenic effects of circadian disruption and highlight its potential role in different human cancers using an epigenetic viewpoint. Finally, the importance of chronotherapy in cancer treatment is highlighted.