Is resilience a unifying concept for the biological sciences?

There is increasing interest in applying resilience concepts at different scales of biological organization to address major interdisciplinary challenges from cancer to climate change. It is unclear, however, whether resilience can be a unifying concept consistently applied across the breadth of the biological sciences, or whether there is limited capacity for integration. In this review, we draw on literature from molecular biology to community ecology to ascertain commonalities and shortcomings in how resilience is measured and interpreted. Resilience is studied at all levels of biological organization, although the term is often not used. There is a suite of resilience mechanisms conserved across biological scales, and there are tradeoffs that affect resilience. Resilience is conceptually useful to help diverse researchers think about how biological systems respond to perturbations, but we need a richer lexicon to describe the diversity of perturbations, and we lack widely applicable metrics of resilience.

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

BACKGROUND

The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems.

RESULTS

BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers.

CONCLUSIONS

BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

Deciphering the similarities and disparities of molecular mechanisms behind respiratory epithelium response to HCoV-229E and SARS-CoV-2 and drug repurposing, a systems biology approach

PURPOSE

Identifying the molecular mechanisms behind SARS-CoV-2 disparities and similarities will help find new treatments. The present study determines networks' shared and non-shared (specific) crucial elements in response to HCoV-229E and SARS-CoV-2 viruses to recommend candidate medications.

METHODS

We retrieved the omics data on respiratory cells infected with HCoV-229E and SARS-CoV-2, constructed PPIN and GRN, and detected clusters and motifs. Using a drug-gene interaction network, we determined the similarities and disparities of mechanisms behind their host response and drug-repurposed.

RESULTS

CXCL1, KLHL21, SMAD3, HIF1A, and STAT1 were the shared DEGs between both viruses' protein-protein interaction network (PPIN) and gene regulatory network (GRN). The NPM1 was a specific critical node for HCoV-229E and was a Hub-Bottleneck shared between PPI and GRN in HCoV-229E. The HLA-F, ADCY5, TRIM14, RPF1, and FGA were the seed proteins in subnetworks of the SARS-CoV-2 PPI network, and HSPA1A and RPL26 proteins were the seed in subnetworks of the PPI network of HCOV-229E. TRIM14, STAT2, and HLA-F played the same role for SARS-CoV-2. Top enriched KEGG pathways included cell cycle and proteasome in HCoV-229E and RIG-I-like receptor, Chemokine, Cytokine-cytokine, NOD-like receptor, and TNF signaling pathways in SARS-CoV-2. We suggest some candidate medications for COVID-19 patient lungs, including Noscapine, Isoetharine mesylate, Cycloserine, Ethamsylate, Cetylpyridinium, Tretinoin, Ixazomib, Vorinostat, Venetoclax, Vorinostat, Ixazomib, Venetoclax, and epoetin alfa for further in-vitro and in-vivo investigations.

CONCLUSION

We suggested CXCL1, KLHL21, SMAD3, HIF1A, and STAT1, ADCY5, TRIM14, RPF1, and FGA, STAT2, and HLA-F as critical genes and Cetylpyridinium, Cycloserine, Noscapine, Ethamsylate, Epoetin alfa, Isoetharine mesylate, Ribavirin, and Tretinoin drugs to study further their importance in treating COVID-19 lung complications.

ΔNp63 regulates MDSC survival and metabolism in triple-negative breast cancer

Triple-negative breast cancer (TNBC) contributes greatly to mortality of breast cancer, demanding new targetable options. We have shown that TNBC patients have high ΔNp63 expression in tumors. However, the function of ΔNp63 in established TNBC is yet to be explored. In current studies, targeting ΔNp63 with inducible CRISPR knockout and Histone deacetylase inhibitor Quisinostat showed that ΔNp63 is important for tumor progression and metastasis in established tumors by promoting myeloid-derived suppressor cell (MDSC) survival through tumor necrosis factor alpha. Decreasing ΔNp63 levels are associated with decreased CD4+ and FOXP3+ T-cells but increased CD8+ T-cells. RNA sequencing analysis indicates that loss of ΔNp63 alters multiple MDSC properties such as lipid metabolism, chemotaxis, migration, and neutrophil degranulation besides survival. We further demonstrated that targeting ΔNp63 sensitizes chemotherapy. Overall, we showed that ΔNp63 reprograms the MDSC-mediated immunosuppressive functions in TNBC, highlighting the benefit of targeting ΔNp63 in chemotherapy-resistant TNBC.

The RNA binding proteins LARP4A and LARP4B promote sarcoma and carcinoma growth and metastasis

RNA-binding proteins (RBPs) are emerging as important regulators of cancer pathogenesis. We reveal that the RBPs LARP4A and LARP4B are differentially overexpressed in osteosarcoma and osteosarcoma lung metastases, as well as in prostate cancer. Depletion of LARP4A and LARP4B reduced tumor growth and metastatic spread in xenografts, as well as inhibiting cell proliferation, motility, and migration. Transcriptomic profiling and high-content multiparametric analyses unveiled a central role for LARP4B, but not LARP4A, in regulating cell cycle progression in osteosarcoma and prostate cancer cells, potentially through modulating key cell cycle proteins such as Cyclins B1 and E2, Aurora B, and E2F1. This first systematic comparison between LARP4A and LARP4B assigns new pro-tumorigenic functions to LARP4A and LARP4B in bone and prostate cancer, highlighting their similarities while also indicating distinct functional differences. Uncovering clear biological roles for these paralogous proteins provides new avenues for identifying tissue-specific targets and potential druggable intervention.

Modeling gene expression cascades during cell state transitions

During cellular processes such as differentiation or response to external stimuli, cells exhibit dynamic changes in their gene expression profiles. Single-cell RNA sequencing (scRNA-seq) can be used to investigate these dynamic changes. To this end, cells are typically ordered along a pseudotemporal trajectory which recapitulates the progression of cells as they transition from one cell state to another. We infer transcriptional dynamics by modeling the gene expression profiles in pseudotemporally ordered cells using a Bayesian inference approach. This enables ordering genes along transcriptional cascades, estimating differences in the timing of gene expression dynamics, and deducing regulatory gene interactions. Here, we apply this approach to scRNA-seq datasets derived from mouse embryonic forebrain and pancreas samples. This analysis demonstrates the utility of the method to derive the ordering of gene dynamics and regulatory relationships critical for proper cellular differentiation and maturation across a variety of developmental contexts.

Detailed survey of an in vitro intestinal epithelium model by single-cell transcriptomics

The co-culture of two adult human colorectal cancer cell lines, Caco-2 and HT29, on Transwell is commonly used as an in vitro gut mimic, yet the translatability of insights from such a system to adult human physiological contexts is not fully characterized. Here, we used single-cell RNA sequencing on the co-culture to obtain a detailed survey of cell type heterogeneity in the system and conducted a holistic comparison with human physiology. We identified the intestinal stem cell-, transit amplifying-, enterocyte-, goblet cell-, and enteroendocrine-like cells in the system. In general, the co-culture was fetal intestine-like, with less variety of gene expression compared to the adult human gut. Transporters for major types of nutrients were found in the majority of the enterocytes-like cells in the system. TLR 4 was not expressed in the sample, indicating that the co-culture model is incapable of mimicking the innate immune aspect of the human epithelium.

Long-lived proteins and DNA as candidate predictive biomarkers for tissue associated diseases

Protein turnover is an important mechanism to maintain proteostasis. Long-lived proteins (LLPs) are vulnerable to lose their function due to time-accumulated damages. In this study we employed in vivo stable isotope labeling in mice from birth to postnatal day 89. Quantitative proteomics analysis of ten tissues and plasma identified 2113 LLPs, including widespread and tissue-specific ones. Interestingly, a significant percentage of LLPs was detected in plasma, implying a potential link to age-related cardiovascular diseases. LLPs identified in brains were related to neurodegenerative diseases. In addition, the relative quantification of DNA-derived deoxynucleosides from the same tissues provided information about cellular DNA renewal and showed good correlation with LLPs in the brain. The combined data reveal tissue-specific maps of mouse LLPs that may be involved in pathology due to a low renewal rate and an increased risk of damage. Tissue-derived peripheral LLPs hold promise as biomarkers for aging and age-related diseases.

Serial adaptive laboratory evolution enhances mixed carbon metabolic capacity of Escherichia coli

Microbes have inherent capacities for utilizing various carbon sources, however they often exhibit sub-par fitness due to low metabolic efficiency. To test whether a bacterial strain can optimally utilize multiple carbon sources, Escherichia coli was serially evolved in L-lactate and glycerol. This yielded two end-point strains that evolved first in L-lactate then in glycerol, and vice versa. The end-point strains displayed a universal growth advantage on single and a mixture of adaptive carbon sources, enabled by a concerted action of carbon source-specialists and generalist mutants. The combination of just four variants of glpK, ppsA, ydcI, and rph-pyrE, accounted for more than 80% of end-point strain fitness. In addition, machine learning analysis revealed a coordinated activity of transcriptional regulators imparting condition-specific regulation of gene expression. The effectiveness of the serial adaptive laboratory evolution (ALE) scheme in bioproduction applications was assessed under single and mixed-carbon culture conditions, in which serial ALE strain exhibited superior productivity of acetoin compared to ancestral strains. Together, systems-level analysis elucidated the molecular basis of serial evolution, which hold potential utility in bioproduction applications.

Exploring gene regulation and biological processes in insects: Insights from omics data using gene regulatory network models

Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy.

Transcriptomic analysis identifies dysregulated pathways and therapeutic targets in PMM2-CDG

PMM2-CDG (MIM # 212065), the most common congenital disorder of glycosylation, is caused by the deficiency of phosphomannomutase 2 (PMM2). It is a multisystemic disease of variable severity that particularly affects the nervous system; however, its molecular pathophysiology remains poorly understood. Currently, there is no effective treatment. We performed an RNA-seq based transcriptomic study using patient-derived fibroblasts to gain insight into the mechanisms underlying the clinical symptomatology and to identify druggable targets. Systems biology methods were used to identify cellular pathways potentially affected by PMM2 deficiency, including Senescence, Bone regulation, Cell adhesion and Extracellular Matrix (ECM) and Response to cytokines. Functional validation assays using patients' fibroblasts revealed defects related to cell proliferation, cell cycle, the composition of the ECM and cell migration, and showed a potential role of the inflammatory response in the pathophysiology of the disease. Furthermore, treatment with a previously described pharmacological chaperone reverted the differential expression of some of the dysregulated genes. The results presented from transcriptomic data might serve as a platform for identifying therapeutic targets for PMM2-CDG, as well as for monitoring the effectiveness of therapeutic strategies, including pharmacological candidates and mannose-1-P, drug repurposing.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

Multiscale computational modeling of aortic valve calcification

Calcific aortic valve disease (CAVD) is a common cardiovascular disease that affects millions of people worldwide. The disease is characterized by the formation of calcium nodules on the aortic valve leaflets, which can lead to stenosis and heart failure if left untreated. The pathogenesis of CAVD is still not well understood, but involves several signaling pathways, including the transforming growth factor beta (TGF β ) pathway. In this study, we developed a multiscale computational model for TGF β -stimulated CAVD. The model framework comprises cellular behavior dynamics, subcellular signaling pathways, and tissue-level diffusion fields of pertinent chemical species, where information is shared among different scales. Processes such as endothelial to mesenchymal transition (EndMT), fibrosis, and calcification are incorporated. The results indicate that the majority of myofibroblasts and osteoblast-like cells ultimately die due to lack of nutrients as they become trapped in areas with higher levels of fibrosis or calcification, and they subsequently act as sources for calcium nodules, which contribute to a polydispersed nodule size distribution. Additionally, fibrosis and calcification processes occur more frequently in regions closer to the endothelial layer where the cell activity is higher. Our results provide insights into the mechanisms of CAVD and TGF β signaling and could aid in the development of novel therapeutic approaches for CAVD and other related diseases such as cancer. More broadly, this type of modeling framework can pave the way for unraveling the complexity of biological systems by incorporating several signaling pathways in subcellular models to simulate tissue remodeling in diseases involving cellular mechanobiology.

Identification of metabolic pathways and key genes associated with atypical parkinsonism using a systems biology approach

Atypical parkinsonism (AP) is a group of complex neurodegenerative disorders with marked clinical and pathophysiological heterogeneity. The use of systems biology tools may contribute to the characterization of hub-bottleneck genes, and the identification of its biological pathways to broaden the understanding of the bases of these disorders. A systematic search was performed on the DisGeNET database, which integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. The tools STRING 11.0 and Cytoscape 3.8.2 were used for analysis of protein-protein interaction (PPI) network. The PPI network topography analyses were performed using the CytoHubba 0.1 plugin for Cytoscape. The hub and bottleneck genes were inserted into 4 different sets on the InteractiveVenn. Additional functional enrichment analyses were performed to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology for a described set of genes. The systematic search in the DisGeNET database identified 485 genes involved with Atypical Parkinsonism. Superimposing these genes, we detected a total of 31 hub-bottleneck genes. Moreover, our functional enrichment analyses demonstrated the involvement of these hub-bottleneck genes in 3 major KEGG pathways. We identified 31 highly interconnected hub-bottleneck genes through a systems biology approach, which may play a key role in the pathogenesis of atypical parkinsonism. The functional enrichment analyses showed that these genes are involved in several biological processes and pathways, such as the glial cell development, glial cell activation and cognition, pathways were related to Alzheimer disease and Parkinson disease. As a hypothesis, we highlight as possible key genes for AP the MAPT (microtubule associated protein tau), APOE (apolipoprotein E), SNCA (synuclein alpha) and APP (amyloid beta precursor protein) genes.

Reduction in metabolic noise reveals rejuvenation following transient severe caloric restriction

Among land vertebrates, the laying hen stands out due to its great reproductive efficiency: producing an egg daily all year long. This production rate makes the laying hen a special model animal to study the general process of reproduction and aging. One unique aspect of hens is their ability to undergo reproductive plasticity and to rejuvenate their reproductive tract during molting, a standard industrial feed restriction protocol for transiently pausing reproduction, followed by improved laying efficiency almost to peak production. Here we use longitudinal metabolomics, immunology, and physiological assays to show that molting promotes reproduction, compresses morbidity, and restores youthfulness when applied to old hens. We identified circulating metabolic biomarkers that quantitatively predict the reproduction and age of individuals. Lastly, we introduce metabolic noise, a robust, unitless, and quantifiable measure for heterogeneity of the complete metabolome as a general marker that can indicate the rate of aging of a population. Indeed, metabolic noise increased with age in control hens, whereas molted hens exhibited reduced noise following molting, indicating systemic rejuvenation. Our results suggest that metabolic noise can be used as a quick and universal proxy for assessing successful aging treatments, accelerating the timeline for drug development.

Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection

Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene-phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype-gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.

Personalized cardiovascular risk assessment in Rheumatoid Arthritis patients using circulating molecular profiles and their modulation by TNFi, IL6Ri, and JAKinibs

BACKGROUND & OBJECTIVES

This study aimed to: 1) analyze the inflammatory profile of Rheumatoid Arthritis (RA) patients, identifying clinical phenotypes associated with cardiovascular (CV) risk; 2) evaluate biologic and targeted-synthetic disease-modifying antirheumatic drugs (b-DMARDs and ts-DMARDs': TNFi, IL6Ri, JAKinibs) effects; and 3) characterize molecular mechanisms in immune-cell activation and endothelial dysfunction.

PATIENTS & METHODS

A total of 387 RA patients and 45 healthy donors were recruited, forming three cohorts: i) 208 RA patients with established disease but without previous CV events; ii) RA-CVD: 96 RA patients with CV events, and iii) 83 RA patients treated with b-DMARDs/ts-DMARDs for 6 months. Serum inflammatory profiles (cytokines/chemokines/growth factors) and NETosis/oxidative stress-linked biomolecules were evaluated. Mechanistic in vitro studies were performed on monocytes, neutrophils and endothelial cells (EC).

RESULTS

In the first RA-cohort, unsupervised clustering unveiled three distinct groups: cluster 3 (C3) displayed the highest inflammatory profile, significant CV-risk score, and greater atheroma plaques prevalence. In contrast, cluster 1 (C1) exhibited the lowest inflammatory profile and CV risk score, while cluster 2 (C2) displayed an intermediate phenotype. Notably, 2nd cohort RA-CVD patients mirrored C3's inflammation. Treatment with b-DMARDs or ts-DMARDs effectively reduced disease-activity scores (DAS28) and restored normal biomolecules levels, controlling CV risk. In vitro, serum from C3-RA or RA-CVD patients increased neutrophils activity and CV-related protein levels in cultured monocytes and EC, which were partially prevented by pre-incubation with TNFi, IL6Ri, and JAKinibs.

CONCLUSIONS

Overall, analyzing circulating molecular profiles in RA patients holds potential for personalized clinical management, addressing CV risk and assisting healthcare professionals in tailoring treatment, ultimately improving outcomes.

Computational drug repurposing for primary hyperparathyroidism

In hyperparathyroidism (hyperPTH), excessive amounts of PTH are secreted, interfering with calcium regulation in the body. Several drugs can control the disease's side effects, but none of them is an alternative treatment to surgery. Therefore, new drug candidates are necessary. In this study, three computationally repositioned drugs, DG 041, IMD 0354, and cucurbitacin I, are evaluated in an in vitro model of hyperPTH. First, we integrated publicly available transcriptomics datasets to propose drug candidates. Using 3D spheroids derived from a single primary hyperPTH patient, we assessed their in vitro efficacy. None of the proposed drugs affected the viability of healthy cell control (HEK293) or overactive parathyroid cells at the level of toxicity. This behavior was attributed to the non-cancerous nature of the parathyroid cells, establishing the hyperPTH disease model. Cucurbitacin I and IMD 0354 exhibited a slight inverse relationship between increased drug concentrations and cell viability, whereas DG 041 increased viability. Based on these results, further studies are needed on the mechanism of action of the repurposed drugs, including determining the effects of these drugs on cellular PTH synthesis and secretion and on the metabolic pathways that regulate PTH secretion.

Progress in polystyrene biodegradation by insect gut microbiota

Polystyrene (PS) is frequently used in the plastics industry. However, its structural stability and difficulty to break down lead to an abundance of plastic waste in the environment, resulting in micro-nano plastics (MNPs). As MNPs are severe hazards to both human and environmental health, it is crucial to develop innovative treatment technologies to degrade plastic waste. The biodegradation of plastics by insect gut microorganisms has gained attention as it is environmentally friendly, efficient, and safe. However, our knowledge of the biodegradation of PS is still limited. This review summarizes recent research advances on PS biodegradation by gut microorganisms/enzymes from insect larvae of different species, and schematic pathways of the degradation process are discussed in depth. Additionally, the prospect of using modern biotechnology, such as genetic engineering and systems biology, to identify novel PS-degrading microbes/functional genes/enzymes and to realize new strategies for PS biodegradation is highlighted. Challenges and limitations faced by the application of genetically engineered microorganisms (GEMs) and multiomics technologies in the field of plastic pollution bioremediation are also discussed. This review encourages the further exploration of the biodegradation of PS by insect gut microbes/enzymes, offering a cutting-edge perspective to identify PS biodegradation pathways and create effective biodegradation strategies.

Protocol for detecting rare and common genetic associations in whole-exome sequencing studies using MAGICpipeline

Whole-exome sequencing (WES) is a major approach to uncovering gene-disease associations and pinpointing effector genes. Here, we present a protocol for estimating genetic associations of rare and common variants in large-scale case-control WES studies using MAGICpipeline, an open-access analysis pipeline. We describe steps for assessing gene-based rare-variant association analyses by incorporating multiple variant pathogenic annotations and statistical techniques. We then detail procedures for identifying disease-related modules and hub genes using weighted correlation network analysis, a systems biology approach. For complete details on the use and execution of this protocol, please refer to Su et al. (2023).1.

Protocol for analyzing emergence dynamics of diabetes with obesity using numerical continuation and bifurcation analysis

Type 2 diabetes (T2D) is a multifactorial disease that slowly and inconspicuously progresses over years. Here, we present a protocol for analyzing slow progression dynamics of T2D with obesity. We describe steps for using software to exploit the differences between the timescales of the metabolic variables and using numerical continuation and bifurcation analysis. We detail procedures to analyze bi-stable system dynamics and identify the thresholds that separate healthy and diabetic states. For complete details on the use and execution of this protocol, please refer to Yildirim et al. (2023).1.

Protocol for organelle-specific cysteine capture and quantification of cysteine oxidation state

Pinpointing functional, structural, and redox-sensitive cysteines is a central challenge of chemoproteomics. Here, we present a protocol comprising two dual-enrichment cysteine chemoproteomic techniques that enable capture of cysteines (Cys-LoC) and quantification of cysteine oxidation state (Cys-LOx) in a localization-specific manner. We describe steps for utilizing TurboID-mediated protein biotinylation for enrichment of compartment-specific proteins, followed by click-mediated biotinylation and enrichment of cysteine-containing peptides. Thus, changes to compartment-specific cysteine identification and redox state can be assessed in a variety of contexts. For complete details on the use and execution of this protocol, please refer to Yan et al. (2023).1.

Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats

Effective neural stimulation requires adequate parametrization. Gaussian-process (GP)-based Bayesian optimization (BO) offers a framework to discover optimal stimulation parameters in real time. Here, we first provide a general protocol to deploy this framework in neurostimulation interventions and follow by exemplifying its use in detail. Specifically, we describe the steps to implant rats with multi-channel electrode arrays in the hindlimb motor cortex. We then detail how to utilize the GP-BO algorithm to maximize evoked target movements, measured as electromyographic responses. For complete details on the use and execution of this protocol, please refer to Bonizzato and colleagues (2023).1.

Protocol for functional profiling of patient-derived organoids for precision oncology

Functional precision oncology-a strategy based on perturbing primary tumor cells from cancer patients-could provide a road forward for personalized treatment. Here, we present a comprehensive protocol covering generation and culture of patient-derived colorectal organoids, isolation and expansion of tumor-infiltrating lymphocytes (TILs), and isolation and culture of peripheral blood mononuclear cells (PBMCs). With this protocol, samples fulfilling the demands for performing multi-omics analysis, e.g., RNA sequencing (RNA-seq), whole-exome sequencing (WES), single-cell RNA sequencing (scRNA-seq), and (phospho-)proteomics, can be generated. For complete details on the use and execution of this protocol, please refer to Plattner et al. (2023).1.

Protocol for a placenta-on-a-chip model using trophoblasts differentiated from human induced pluripotent stem cells

Organ-on-a-chip technology incorporating stem cell techniques represents a promising strategy to improve modeling of human organs. Here, we present a protocol for generating a standardized 3D placenta-on-a-chip model using trophoblast derived from human induced pluripotent stem cells (hiPSCs). We describe steps for seeding hiPSCs into multi-chip OrganoPlate devices and on-chip differentiation into trophoblasts against an extracellular matrix under perfused conditions. We then detail procedures for conducting a functional barrier integrity assay, immunostaining, and collecting protein or RNA for molecular analysis. For complete details on the use and execution of this protocol, please refer to Lermant et al. (2023).1.

Cross-sectional analyses of metabolites across biological samples mediating dietary acid load and chronic kidney disease

Chronic kidney disease (CKD) is a major public health burden, with dietary acid load (DAL) and gut microbiota playing crucial roles. As DAL can affect the host metabolome, potentially via the gut microbiota, we cross-sectionally investigated the interplay between DAL, host metabolome, gut microbiota, and early-stage CKD (TwinsUK, n = 1,453). DAL was positively associated with CKD stage G1-G2 (Beta (95% confidence interval) = 0.34 (0.007; 0.7), p = 0.046). After adjusting for covariates and multiple testing, we identified 15 serum, 14 urine, 8 stool, and 7 saliva metabolites, primarily lipids and amino acids, associated with both DAL and CKD progression. Of these, 8 serum, 2 urine, and one stool metabolites were found to mediate the DAL-CKD association. Furthermore, the stool metabolite 5-methylhexanoate (i7:0) correlated with 26 gut microbial species. Our findings emphasize the gut microbiota's therapeutic potential in countering DAL's impact on CKD through the host metabolome. Interventional and longitudinal studies are needed to establish causality.

Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio

Seasonality in laboratory healthcare data is associated with possible under- and overdiagnoses of patients in the clinic. Here, we present a protocol to analyze electronic health record data for seasonality patterns and adjust existing reference intervals for these changes using R software. We describe steps for preprocessing population-wide patient laboratory data into a single dataset. We then detail steps for defining strata, normalizing to median, and fitting data to selected functions. For complete details on the use and execution of this protocol, please refer to Muse et al. (2023).1.

Differential alternative splicing landscape identifies potentially functional RNA binding proteins in early embryonic development in mammals

Alternative splicing (AS) as one of the important post-transcriptional regulatory mechanisms has been poorly studied during embryogenesis. In this study, we comprehensively collected and analyzed the transcriptome data of early embryos from human and mouse. We found that AS plays an important role in this process and predicted candidate RNA binding protein (RBP) regulators that are associated with reproductive development. The predicted RBPs such as EIF4A3, MAK16, SRSF2, and UTP23 were found to be associated with reproductive disorders. By Smart-seq2 sequencing analysis, we identified 5445 aberrant alternative splicing events in Eif4a3-knockdown embryos. These events were preferentially associated with RNA processing. In conclusion, our work on the landscape and potential function of alternative splicing events will boost further investigation of detailed mechanisms and key factors regulating mammalian early embryo development and promote the inspiration of pharmaceutical approaches for disorders in this crucial biology process.

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity-related biological features can effectively screen high-risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity-gene clinical prognostic index (LOG-CPI), combining a 7-gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5-year accuracy = 0.828, 0.760, 0.751, and 0.792, respectively). LOG-CPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.

Protocol for FRAP-based estimation of nuclear import and export rates in single yeast cells

Here, we present a protocol for estimating nuclear transport parameters in single cells. We describe steps for performing four consecutive fluorescence recovery after photobleaching experiments, fitting the obtained data to an ordinary differential equations model, and statistical analysis of the fittings using a specialized R package. This protocol permits the estimation of import and export rates, nuclear or cytosolic fixed fractions, and total number of molecules. For complete details on the use and execution of this protocol, please refer to Durrieu et al.1.

Protocol to isolate nuclei from Chlamydomonas reinhardtii for ATAC sequencing

The isolation of sufficient amounts of intact nuclei is essential to obtain high-resolution maps of chromatin accessibility via assay for transposase-accessible chromatin using sequencing (ATAC-seq). Here, we present a protocol for tag-free isolation of nuclei from both cell walled and cell wall-deficient strains of the green model alga Chlamydomonas reinhardtii at a suitable quality for ATAC-seq. We describe steps for nuclei isolation, quantification, and downstream ATAC-seq. This protocol is optimized to shorten the time of isolation and quantification of nuclei.

Protocol to train a support vector machine for the automatic curation of bacterial cell detections in microscopy images

Manual curation of bacterial cell detections in microscopy images remains a time-consuming and laborious task. This work offers a comprehensive, step-by-step tutorial on training a support vector machine to autonomously distinguish between good and bad cell detections. Jupyter notebooks are included to perform feature extraction, labeling, and training of the machine learning model. This method can readily be incorporated into profiling pipelines aimed at extracting a multitude of features across large collections of individual cells, strains, and species. For complete details on the use and execution of this protocol, please refer to Govers et al.1.

Revealing the dynamics and mechanisms of bacterial interactions in cheese production with metabolic modelling

Cheese taste and flavour properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This model accurately predicts the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration revealed additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added value of dynamic metabolic modeling for the comprehension of fermented food processes.

Digging into the intrinsic capacity concept: Can it be applied to Alzheimer's disease?

Historically, aging research has largely centered on disease pathology rather than promoting healthy aging. The World Health Organization's (WHO) policy framework (2015-2030) underscores the significance of fostering the contributions of older individuals to their families, communities, and economies. The WHO has introduced the concept of intrinsic capacity (IC) as a key metric for healthy aging, encompassing five primary domains: locomotion, vitality, sensory, cognitive, and psychological. Past AD research, constrained by methodological limitations, has focused on single outcome measures, sidelining the complexity of the disease. Our current scientific milieu, however, is primed to adopt the IC concept. This is due to three critical considerations: (I) the decline in IC is linked to neurocognitive disorders, including AD, (II) cognition, a key component of IC, is deeply affected in AD, and (III) the cognitive decline associated with AD involves multiple factors and pathophysiological pathways. Our study explores the application of the IC concept to AD patients, offering a comprehensive model that could revolutionize the disease's diagnosis and prognosis. There is a dearth of information on the biological characteristics of IC, which are a result of complex interactions within biological systems. Employing a systems biology approach, integrating omics technologies, could aid in unraveling these interactions and understanding IC from a holistic viewpoint. This comprehensive analysis of IC could be leveraged in clinical settings, equipping healthcare providers to assess AD patients' health status more effectively and devise personalized therapeutic interventions in accordance with the precision medicine paradigm. We aimed to determine whether the IC concept could be extended from older individuals to patients with AD, thereby presenting a model that could significantly enhance the diagnosis and prognosis of this disease.

The evolving world of milk oligosaccharides: Biochemical diversity understood by computational advances

Milk oligosaccharides, complex carbohydrates unique to mammalian milk, play crucial roles in infant nutrition and immune development. This review explores their biochemical diversity, tracing the evolutionary paths that have led to their variation across different species. We highlight the intersection of nutrition, biology, and chemistry in understanding these compounds. Additionally, we discuss the latest computational methods and analytical techniques that have revolutionized the study of milk oligosaccharides, offering insights into their structural complexity and functional roles. This brief but essential review not only aims to provide a deeper understanding of milk oligosaccharides but also discuss the road toward their potential applications.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Systems biology of B cells in COVID-19

The integration of multi-'omic datasets into complex systems-wide assessments has become a mainstay in immunologic investigation. This focus on high-dimensional data collection and analysis was on full display in the investigation of COVID-19, the respiratory illness resulting from infection by the novel coronavirus SARS-CoV-2. Particularly in the area of B cell biology, tremendous efforts in both cellular and serologic investigation have resulted in an increasingly detailed mapping of the coordinated effector, memory, and antibody secreting cell responses that underpin the development of humoral immunity in response to primary viral infection. Further, the rapid development and deployment of effective vaccines has allowed for the assessment of developing memory responses across a wide variety of immune contexts, including in patients with compromised immune function. The result has been a period of rapid gains in the understanding of B cell biology unrestricted to the study of COVID-19. Here, we outline the systems-level technologies that have been routinely implemented in these investigations throughout the pandemic, and discuss how their use has led to clear and applicable gains in pursuance of the amelioration of human infectious disease and beyond.

The role of MAPK, notch and Wnt signaling pathways in papillary thyroid cancer: Evidence from a systematic review and meta-analyzing microarray datasets employing bioinformatics knowledge and literature

Papillary thyroid cancer (PTC) is a prevalent kind of thyroid cancer (TC), with the risk of metastasis increasing faster than any other malignancy. So, understanding the role of PTC in pathogenesis requires studying the various gene expressions to find out which particular molecular biomarkers will be helpful. The authors conducted a comprehensive search on the PubMed microarray database and a meta-analysis approach on the remaining ones to determine the differentially expressed genes between PTC and normal tissues, along with the analyses of overall survival (OS) and recurrence-free survival (RFS) rates in patients with PTC. We considered the associated genes with MAPK, Wnt, and Notch signaling pathways. Two GEO datasets have been included in this research, considering inclusion and exclusion criteria. Nineteen genes were found to have higher differences through the meta-analysis procedure. Among them, ten genes were upregulated, and nine genes were downregulated. The expression of 19 genes was examined using the GEPIA2 database, and the Kaplan-Meier plot statistics were used to analyze RFS and the OS rates. We discovered seven significant genes with the validation: PRICKLE1, KIT, RPS6KA5, GADD45B, FGFR2, FGF7, and DTX4. To further explain these findings, it was discovered that the mRNA expression levels of these seven genes and the remaining 12 genes were shown to be substantially linked with the results of the experimental literature investigations on the PTC. Our research found nineteen panels of genes that could be involved in the PTC progression and metastasis and the immune system infiltration of these cancers.

ViCEKb: Vitiligo-linked Chemical Exposome Knowledgebase

Vitiligo is a complex disease wherein the environmental factors, in conjunction with the underlying genetic predispositions, trigger the autoimmune destruction of melanocytes, ultimately leading to depigmented patches on the skin. While genetic factors have been extensively studied, the knowledge on environmental triggers remains sparse and less understood. To address this knowledge gap, we present the first comprehensive knowledgebase of vitiligo-triggering chemicals namely, Vitiligo-linked Chemical Exposome Knowledgebase (ViCEKb). ViCEKb involves an extensive and systematic manual effort in curation of published literature and subsequent compilation of 113 unique chemical triggers of vitiligo. ViCEKb standardizes various chemical information, and categorizes the chemicals based on their evidences and sources of exposure. Importantly, ViCEKb contains a wide range of metrics necessary for different toxicological evaluations. Notably, we observed that ViCEKb chemicals are present in a variety of consumer products. For instance, Propyl gallate is present as a fragrance substance in various household products, and Flutamide is used in medication to treat prostate cancer. These two chemicals have the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Furthermore, an extensive cheminformatics-based investigation revealed that ViCEKb chemical space is structurally diverse and comprises unique chemical scaffolds in comparison with skin specific regulatory lists. For example, Neomycin and 2,3,5-Triglycidyl-4-aminophenol have unique chemical scaffolds and the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Finally, a transcriptomics-based analysis of ViCEKb chemical perturbations in skin cell samples highlighted the commonality in their linked biological processes. Overall, we present the first comprehensive effort in compilation and exploration of various chemical triggers of vitiligo. We believe such a resource will enable in deciphering the complex etiology of vitiligo and aid in the characterization of human chemical exposome. ViCEKb is freely available for academic research at: https://cb.imsc.res.in/vicekb.

IPD3, a master regulator of arbuscular mycorrhizal symbiosis, affects genes for immunity and metabolism of non-host Arabidopsis when restored long after its evolutionary loss

Arbuscular mycorrhizal symbiosis (AM) is a beneficial trait originating with the first land plants, which has subsequently been lost by species scattered throughout the radiation of plant diversity to the present day, including the model Arabidopsis thaliana. To explore if elements of this apparently beneficial trait are still present and could be reactivated we generated Arabidopsis plants expressing a constitutively active form of Interacting Protein of DMI3, a key transcription factor that enables AM within the Common Symbiosis Pathway, which was lost from Arabidopsis along with the AM host trait. We characterize the transcriptomic effect of expressing IPD3 in Arabidopsis with and without exposure to the AM fungus (AMF) Rhizophagus irregularis, and compare these results to the AM model Lotus japonicus and its ipd3 knockout mutant cyclops-4. Despite its long history as a non-AM species, restoring IPD3 in the form of its constitutively active DNA-binding domain to Arabidopsis altered expression of specific gene networks. Surprisingly, the effect of expressing IPD3 in Arabidopsis and knocking it out in Lotus was strongest in plants not exposed to AMF, which is revealed to be due to changes in IPD3 genotype causing a transcriptional state, which partially mimics AMF exposure in non-inoculated plants. Our results indicate that molecular connections to symbiosis machinery remain in place in this nonAM species, with implications for both basic science and the prospect of engineering this trait for agriculture.

NatD epigenetically activates FOXA2 expression to promote breast cancer progression by facilitating MMP14 expression

N-α-acetyltransferase D (NatD) mediates N-α-terminal acetylation of histone H4 (Nt-Ac-H4), but its role in breast cancer metastasis remains unknown. Here, we show that depletion of NatD directly represses the expression of FOXA2, and is accompanied by a significant reduction in Nt-Ac-H4 enrichment at the FOXA2 promoter. We show that NatD is commonly upregulated in primary breast cancer tissues, where its expression level correlates with FOXA2 expression, enhanced invasiveness, and poor clinical outcomes. Furthermore, we show that FOXA2 promotes the migration and invasion of breast cancer cells by activating MMP14 expression. MMP14 is also upregulated in breast cancer tissues, where its expression level correlates with FOXA2 expression and poor clinical prognosis. Our study shows that the NatD-FOXA2-MMP14 axis functions as a key signaling pathway to promote the migratory and invasive capabilities of breast cancer cells, suggesting that NatD is a critical epigenetic modulator of cell invasion during breast cancer progression.

A liver digital twin for in silico testing of cellular and inter-cellular mechanisms in regeneration after drug-induced damage

This communication presents a mathematical mechanism-based model of the regenerating liver after drug-induced pericentral lobule damage resolving tissue microarchitecture. The consequence of alternative hypotheses about the interplay of different cell types on regeneration was simulated. Regeneration dynamics has been quantified by the size of the damage-induced dead cell area, the hepatocyte density and the spatial-temporal profile of the different cell types. We use deviations of observed trajectories from the simulated system to identify branching points, at which the systems behavior cannot be explained by the underlying set of hypotheses anymore. Our procedure reflects a successful strategy for generating a fully digital liver twin that, among others, permits to test perturbations from the molecular up to the tissue scale. The model simulations are complementing current knowledge on liver regeneration by identifying gaps in mechanistic relationships and guiding the system toward the most informative (lacking) parameters that can be experimentally addressed.

Unraveling molecular similarities between colorectal polyps and colorectal cancer: a systems biology approach

Background/Aims

Colorectal cancer (CRC) and colorectal polyps are intimately linked, with polyps acting as precursors to CRC. Understanding the molecular mechanisms governing their development is crucial for advancing diagnosis and treatment. Employing a systems biology approach, we investigated the molecular similarities between polyp and CRC.

Methods

We analyzed gene expression profiles, protein-protein interactions, transcription factors, and gene ontology to identify common differentially expressed genes (DEGs) and unravel shared molecular pathways.

Results

Our analysis revealed 520 commonly dysregulated genes in polyps and CRC, serving as potential biomarkers and pivotal contributors to disease progression. Gene ontology analysis elucidated distinct biological processes associated with upregulated and downregulated DEGs in both conditions, highlighting common pathways, including signal transduction, cell adhesion, and positive regulation of cell proliferation. Moreover, protein-protein interaction networks shed light on subnetworks involved in rRNA processing, positive regulation of cell proliferation, mRNA splicing, and cell division. Transcription factor analysis identified major regulators and differentially expressed transcription factors in polyp and CRC. Notably, we identified common differentially expressed transcription factors, including ZNF217, NR3C1, KLF5, GATA6, and STAT3, with STAT3 and NR3C1 exhibiting increased expression.

Conclusions

This comprehensive analysis enriches our understanding of the molecular mechanisms underlying polyp formation and CRC development, providing potential targets for further investigation and therapeutic intervention. Our findings contribute substantively to crafting personalized strategies for refining the diagnosis and treatment of polyps and CRC.

An ensemble of bioinformatics and machine learning approaches to identify shared breast cancer biomarkers among diverse populations

Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.

Plasma and milk metabolomics revealed changes in amino acid metabolism in Holstein dairy cows under heat stress

Our understanding of metabolic alterations triggered by heat stress is incomplete, which limits the designing of nutritional strategies to mitigate negative productive and health effects. Thus, this study aimed to explore the metabolic responses of heat-stressed dairy cows to dietary supplementation with vitamin D3/Ca and vitamin E/Se. Twelve multiparous Holstein cows were enrolled in a split-plot Latin square design with two distinct vitamin E/Se supplementation levels, either at a low (ESe-, n = 6, 11.1 IU/kg vitamin E and 0.55 mg/kg Se) or a high dose (ESe+, n = 6 223 IU/kg vitamin E and 1.8 mg/kg Se) as the main plot. Treatment subplots, arranged in a replicated 3 × 3 Latin square design, comprised heat challenge (Temperature Humidity Index, THI: 72.0-82.0) supplemented with different levels of vitamin D3/Ca: either low (HS/DCa-, 1 012 IU/kg and 0.73%, respectively) or high (HS/DCa+, 3 764 IU/kg and 0.97%, respectively), and a pair-fed control group in thermoneutrality (THI = 61.0-64.0) receiving the low dose of vitamin D3/Ca (TN). The liquid chromatography-mass spectrometry-based metabolome profile was determined in blood plasma and milk sampled at the beginning (day 0) and end (day 14) of each experimental period. The results were analyzed for the effect of (1) TN vs. HS/ESe-/DCa-, and (2) the vitamin E/Se and vitamin D3/Ca supplementation. No group or group × day effects were detected in the plasma metabolome (false discovery rate, FDR > 0.05), except for triglyceride 52:2 being higher (FDR = 0.03) on day 0 than 14. Taurine, creatinine and butyryl-carnitine showed group × day interactions in the milk metabolome (FDR ≤ 0.05) as creatinine (+22%) and butyryl-carnitine (+190%) were increased (P < 0.01) on day 14, and taurine was decreased (-65%, P < 0.01) on day 14 in the heat stress (HS) cows, compared with day 0. Most compounds were unaffected by vitamin E/Se or vitamin D3/Ca supplementation level or their interaction (FDR > 0.05) in plasma and milk, except for milk alanine which was lower (-69%, FDR = 0.03) in the E/Se+ groups, compared with E/Se-. Our results indicated that HS triggered more prominent changes in the milk than in the plasma metabolome, with consistent results in milk suggesting increased muscle catabolism, as reflected by increased creatinine, alanine and citrulline levels. Supplementing with high levels of vitamin E/Se or vitamin D3/Ca or their combination did not appear to affect the metabolic remodeling triggered by HS.

Integrating network pharmacology: The next-generation approach in ocular drug discovery

With the spread of the "omics" sciences, the approaches of systems biology can be considered as new paradigms of pharmacological research for discovery of novel targets and/or treatments for complex multifactorial diseases. Data from omics sciences can be used for the design of biologic networks, that in turn can be quantitatively analyzed to identify new pharmacological targets. In this review, we will introduce the concept of network pharmacology, particularly the application of this innovative approach in the field of ocular pharmacology, with a focus on retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD) and glaucoma.

Insulin signaling and pharmacology in humans and in corals

Once thought to be a unique capability of the Langerhans islets in the pancreas of mammals, insulin (INS) signaling is now recognized as an evolutionarily ancient function going back to prokaryotes. INS is ubiquitously present not only in humans but also in unicellular eukaryotes, fungi, worms, and Drosophila. Remote homologue identification also supports the presence of INS and INS receptor in corals where the availability of glucose is largely dependent on the photosynthetic activity of the symbiotic algae. The cnidarian animal host of corals operates together with a 20,000-sized microbiome, in direct analogy to the human gut microbiome. In humans, aberrant INS signaling is the hallmark of metabolic disease, and is thought to play a major role in aging, and age-related diseases, such as Alzheimer's disease. We here would like to argue that a broader view of INS beyond its human homeostasis function may help us understand other organisms, and in turn, studying those non-model organisms may enable a novel view of the human INS signaling system. To this end, we here review INS signaling from a new angle, by drawing analogies between humans and corals at the molecular level.

Glucocorticoid- and pioglitazone-induced proteinuria reduction in experimental NS both correlate with glomerular ECM modulation

Idiopathic nephrotic syndrome (NS) is a common glomerular disease. Although glucocorticoids (GC) are the primary treatment, the PPARγ agonist pioglitazone (Pio) also reduces proteinuria in patients with NS and directly protects podocytes from injury. Because both drugs reduce proteinuria, we hypothesized these effects result from overlapping transcriptional patterns. Systems biology approaches compared glomerular transcriptomes from rats with PAN-induced NS treated with GC vs. Pio and identified 29 commonly regulated genes-of-interest, primarily involved in extracellular matrix (ECM) remodeling. Correlation with clinical idiopathic NS patient datasets confirmed glomerular ECM dysregulation as a potential mechanism of injury. Cellular deconvolution in silico revealed GC- and Pio-induced amelioration of altered genes primarily within podocytes and mesangial cells. While validation studies are indicated, these analyses identified molecular pathways involved in the early stages of NS (prior to scarring), suggesting that targeting glomerular ECM dysregulation may enable a future non-immunosuppressive approach for proteinuria reduction in idiopathic NS.

A critical signal for phenotype transition driven by negative feedback loops

The biological rhythms governed by negative feedback loops have undergone extensive investigation. However, developing reliable and versatile warning signals to predict periodic fluctuations in physiological processes and behaviors associated with these rhythms remains a challenge. Here, we monitored the heart rate and tracked ovulation dates of 91 fertile women. The finding strongly links the velocity (derivative) of heart rate with ovulation in menstrual cycles, providing a predictive warning signal. Similarly, an analysis of calcium signaling in the suprachiasmatic nucleus (SCN) of mice reveals that the maximum velocity of rising calcium signal aligns with locomotor activity offsets. To demonstrate the generality of derivative-transitions link, numerical simulations using a negative feedback loop model were conducted. Statistical analysis indicated that over 90% of the oscillations exhibited a correlation between maximum velocity and transition points. Consequently, the maximum velocity derived from oscillatory curves holds significant potential as an early warning signal for critical transitions.

Atrial natriuretic peptide signaling co-regulates lipid metabolism and ventricular conduction system gene expression in the embryonic heart

It has been shown that atrial natriuretic peptide (ANP) and its high affinity receptor (NPRA) are involved in the formation of ventricular conduction system (VCS). Inherited genetic variants in fatty acid oxidation (FAO) genes are known to cause conduction abnormalities in newborn children. Although the effect of ANP on energy metabolism in noncardiac cell types is well documented, the role of lipid metabolism in VCS cell differentiation via ANP/NPRA signaling is not known. In this study, histological sections and primary cultures obtained from E11.5 mouse ventricles were analyzed to determine the role of metabolic adaptations in VCS cell fate determination and maturation. Exogenous treatment of E11.5 ventricular cells with ANP revealed a significant increase in lipid droplet accumulation, FAO and higher expression of VCS marker Cx40. Using specific inhibitors, we further identified PPARγ and FAO as critical downstream regulators of ANP-mediated regulation of metabolism and VCS formation.