Uncovering the regeneration strategies of zebrafish organs: a comprehensive systems biology study on heart, cerebellum, fin, and retina regeneration

Genetic modeling of degenerative diseases and mechanisms of neuronal regeneration in the zebrafish cerebellum

The cerebellum is a highly conserved brain compartment of vertebrates. Genetic diseases of the human cerebellum often lead to degeneration of the principal neuron, the Purkinje cell, resulting in locomotive deficits and socio-emotional impairments. Due to its relatively simple but highly conserved neuroanatomy and circuitry, these human diseases can be modeled well in vertebrates amenable for genetic manipulation. In the recent years, cerebellar research in zebrafish has contributed to understanding cerebellum development and function, since zebrafish larvae are not only molecularly tractable, but also accessible for high resolution in vivo imaging due to the transparency of the larvae and the ease of access to the zebrafish cerebellar cortex for microscopy approaches. Therefore, zebrafish is increasingly used for genetic modeling of human cerebellar neurodegenerative diseases and in particular of different types of Spinocerebellar Ataxias (SCAs). These models are well suited to address the underlying pathogenic mechanisms by means of in vivo cell biological studies. Furthermore, accompanying circuitry characterizations, physiological studies and behavioral analysis allow for unraveling molecular, structural and functional relationships. Moreover, unlike in mammals, zebrafish possess an astonishing ability to regenerate neuronal populations and their functional circuitry in the central nervous system including the cerebellum. Understanding the cellular and molecular processes of these regenerative processes could well serve to counteract acute and chronic loss of neurons in humans. Based on the high evolutionary conservation of the cerebellum these regeneration studies in zebrafish promise to open therapeutic avenues for counteracting cerebellar neuronal degeneration. The current review aims to provide an overview over currently existing genetic models of human cerebellar neurodegenerative diseases in zebrafish as well as neuroregeneration studies using the zebrafish cerebellum. Due to this solid foundation in cerebellar disease modeling and neuronal regeneration analysis, the zebrafish promises to become a popular model organism for both unraveling pathogenic mechanisms of human cerebellar diseases and providing entry points for therapeutic neuronal regeneration approaches.

Cost-effective strategies to knock down genes of interest in the retinas of adult zebrafish

High throughput sequencing has generated an enormous amount of information about the genes expressed in various cell types and tissues throughout the body, and about how gene expression changes over time and in diseased conditions. This knowledge has made targeted gene knockdowns an important tool in screening and identifying the roles of genes that are differentially expressed among specific cells of interest. While many approaches are available and optimized in mammalian models, there are still several limitations in the zebrafish model. In this article, we describe two approaches to target specific genes in the retina for knockdown: cell-penetrating, translation-blocking Vivo-Morpholino oligonucleotides and commercially available lipid nanoparticle reagents to deliver siRNA. We targeted expression of the PCNA gene in the retina of a P23H rhodopsin transgenic zebrafish model, in which rapidly proliferating progenitor cells replace degenerated rod photoreceptors. Retinas collected 48 h after intravitreal injections in adult zebrafish reveal that both Vivo-Morpholinos and lipid encapsulated siRNAs were able to successfully knock down expression of PCNA. However, only retinas injected with Vivo-Morpholinos showed a significant decrease in the formation of P23H rhodopsin-expressing rods, a downstream effect of PCNA inhibition. Surprisingly, Vivo-Morpholinos were able to exit the injected eye and enter the contralateral non-injected eye to inhibit PCNA expression. In this article we describe the techniques, concentrations, and considerations we found necessary to successfully target and inhibit genes through Vivo-Morpholinos and lipid encapsulated siRNAs.

Transcriptomic data meta-analysis reveals common and injury model specific gene expression changes in the regenerating zebrafish heart

Zebrafish have the capacity to fully regenerate the heart after an injury, which lies in sharp contrast to the irreversible loss of cardiomyocytes after a myocardial infarction in humans. Transcriptomics analysis has contributed to dissect underlying signaling pathways and gene regulatory networks in the zebrafish heart regeneration process. This process has been studied in response to different types of injuries namely: ventricular resection, ventricular cryoinjury, and genetic ablation of cardiomyocytes. However, there exists no database to compare injury specific and core cardiac regeneration responses. Here, we present a meta-analysis of transcriptomic data of regenerating zebrafish hearts in response to these three injury models at 7 days post injury (7dpi). We reanalyzed 36 samples and analyzed the differentially expressed genes (DEG) followed by downstream Gene Ontology Biological Processes (GO:BP) analysis. We found that the three injury models share a common core of DEG encompassing genes involved in cell proliferation, the Wnt signaling pathway and genes that are enriched in fibroblasts. We also found injury-specific gene signatures for resection and genetic ablation, and to a lower extent the cryoinjury model. Finally, we present our data in a user-friendly web interface that displays gene expression signatures across different injury types and highlights the importance to consider injury-specific gene regulatory networks when interpreting the results related to cardiac regeneration in the zebrafish. The analysis is freely available at: https://mybinder.org/v2/gh/MercaderLabAnatomy/PUB_Botos_et_al_2022_shinyapp_binder/HEAD?urlpath=shiny/bus-dashboard/ .

A joint multi-modal learning method for early-stage knee osteoarthritis disease classification

Osteoarthritis (OA) is a progressive and chronic disease. Identifying the early stages of OA disease is important for the treatment and care of patients. However, most state-of-the-art methods only use single-modal data to predict disease status, so that these methods usually ignore complementary information in multi-modal data. In this study, we develop an integrated multi-modal learning method (MMLM) that uses an interpretable strategy to select and fuse clinical, imaging, and demographic features to classify the grade of early-stage knee OA disease. MMLM applies XGboost and ResNet50 to extract two heterogeneous features from the clinical data and imaging data, respectively. And then we integrate these extracted features with demographic data. To avoid the negative effects of redundant features in a direct integration of multiple features, we propose a L1-norm-based optimization method (MMLM) to regularize the inter-correlations among the multiple features. MMLM was assessed using the Osteoarthritis Initiative (OAI) data set with machine learning classifiers. Extensive experiments demonstrate that MMLM improves the performance of the classifiers. Furthermore, a visual analysis of the important features in the multimodal data verified the relations among the modalities when classifying the grade of knee OA disease.

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

Oxidative Stress and AKT-Associated Angiogenesis in a Zebrafish Model and Its Potential Application for Withanolides

Oxidative stress and the AKT serine/threonine kinase (AKT) signaling pathway are essential regulators in cellular migration, metastasis, and angiogenesis. More than 300 withanolides were discovered from the plant family Solanaceae, exhibiting diverse functions. Notably, the relationship between oxidative stress, AKT signaling, and angiogenesis in withanolide treatments lacks comprehensive understanding. Here, we summarize connecting evidence related to oxidative stress, AKT signaling, and angiogenesis in the zebrafish model. A convenient vertebrate model monitored the in vivo effects of developmental and tumor xenograft angiogenesis using zebrafish embryos. The oxidative stress and AKT-signaling-modulating abilities of withanolides were highlighted in cancer treatments, which indicated that further assessments of their angiogenesis-modulating potential are necessary in the future. Moreover, targeting AKT for inhibiting AKT and its AKT signaling shows the potential for anti-migration and anti-angiogenesis purposes for future application to withanolides. This particularly holds for investigating the anti-angiogenetic effects mediated by the oxidative stress and AKT signaling pathways in withanolide-based cancer therapy in the future.

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning

ABSTRACT

Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mart for Intensive Care III, we identified 604 and 1,614 patients, respectively. Two popular machine learning algorithms (i.e., extreme gradient boosting [XGBoost] and logistic regression) were applied. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models. By analyzing the feature importance based on XGBoost, we found that features in vital signs (VS), routine blood (RB), and blood gas analysis (BG) were the most relevant to THS (0.292, 0.249, and 0.225, respectively). Thus, the stepped relationships existing in them were revealed. Furthermore, the three stepped feature sets (i.e., VS, VS + RB, and VS + RB + sBG) were passed to the two machine learning algorithms to predict THS in the subsequent T hours (where T = 3, 2, 1, or 0.5), respectively. Results showed that the XGBoost model performance was significantly better than the logistic regression. The model using vital signs alone achieved good performance at the half-hour time window (AUROC = 0.935), and the performance was increased when laboratory results were added, especially when the time window was 1 h (AUROC = 0.950 and 0.968, respectively). These good-performing interpretable models demonstrated acceptable generalization ability in external validation, which could flexibly and rollingly predict THS T hours (where T = 0.5, 1) prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed THS prediction models.

An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19

A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study.

Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression

BACKGROUND AND OBJECTIVES

Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease severity changes through the course of the disease. In that way, predicting disease progression from mild towards moderate, severe and critical condition, would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not necessary.

METHODS

We present a methodology for the classification of patients into 4 distinct categories of the clinical condition of COVID-19 disease. Classification of patients is based on the values of blood biomarkers that were assessed by Gradient boosting regressor and which were selected as biomarkers that have the greatest influence in the classification of patients with COVID-19.

RESULTS

The results show that among several tested algorithms, XGBoost classifier achieved best results with an average accuracy of 94% and an average F1-score of 94.3%. We have also extracted 10 best features from blood analysis that are strongly associated with patient condition and based on those features we can predict the severity of the clinical condition.

CONCLUSIONS

The main advantage of our system is that it is a decision tree-based algorithm which is easier to interpret, instead of the use of black box models, which are not appealing in medical practice.

Loss of zebrafish atp6v1e1b, encoding a subunit of vacuolar ATPase, recapitulates human ARCL type 2C syndrome and identifies multiple pathobiological signatures

The inability to maintain a strictly regulated endo(lyso)somal acidic pH through the proton-pumping action of the vacuolar-ATPases (v-ATPases) has been associated with various human diseases including heritable connective tissue disorders. Autosomal recessive (AR) cutis laxa (CL) type 2C syndrome is associated with genetic defects in the ATP6V1E1 gene and is characterized by skin wrinkles or loose redundant skin folds with pleiotropic systemic manifestations. The underlying pathological mechanisms leading to the clinical presentations remain largely unknown. Here, we show that loss of atp6v1e1b in zebrafish leads to early mortality, associated with craniofacial dysmorphisms, vascular anomalies, cardiac dysfunction, N-glycosylation defects, hypotonia, and epidermal structural defects. These features are reminiscent of the phenotypic manifestations in ARCL type 2C patients. Our data demonstrates that loss of atp6v1e1b alters endo(lyso)somal protein levels, and interferes with non-canonical v-ATPase pathways in vivo. In order to gain further insights into the processes affected by loss of atp6v1e1b, we performed an untargeted analysis of the transcriptome, metabolome, and lipidome in early atp6v1e1b-deficient larvae. We report multiple affected pathways including but not limited to oxidative phosphorylation, sphingolipid, fatty acid, and energy metabolism together with profound defects on mitochondrial respiration. Taken together, our results identify complex pathobiological effects due to loss of atp6v1e1b in vivo.

Cutis laxa syndromes are pleiotropic disorders of the connective tissue, characterized by skin redundancy and variable systemic manifestations. Cutis laxa syndromes are caused by pathogenic variants in genes encoding structural and regulatory components of the extracellular matrix or in genes encoding components of cellular trafficking, metabolism, and mitochondrial function. Pathogenic variants in genes coding for vacuolar-ATPases, a multisubunit complex responsible for the acidification of multiple intracellular vesicles, cause type 2 cutis laxa syndromes, a group of cutis laxa subtypes further characterized by neurological, skeletal, and rarely cardiopulmonary manifestations. To investigate the pathomechanisms of vacuolar-ATPase dysfunction, we generated zebrafish models that lack a crucial subunit of the vacuolar-ATPases. The mutant zebrafish models show morphological and functional features reminiscent of the phenotypic manifestations in cutis laxa patients carrying pathogenic variants in ATP6V1E1. In-depth analysis at multiple -omic levels identified biological signatures that indicate impairment of signaling pathways, lipid metabolism, and mitochondrial respiration. We anticipate that these data will contribute to a better understanding of the pathogenesis of cutis laxa syndromes and other disorders involving defective v-ATPase function, which may eventually improve patient treatment and management.

Expression analysis of Hsp90α and cytokines in zebrafish caudal fin regeneration

Zebrafish (Danio rerio) is an ideal model organism for exploring the ability and mechanism of tissue regeneration in the vertebrate. However, the specific cellular and molecular mechanism of caudal fin regeneration in zebrafish remains largely unclear. Therefore, we first confirmed the crucial period of fin regeneration in adult zebrafish by morphological and histological analysis. Then we performed RNA-Seq analysis of the caudal fin regeneration at three key stages, which provided some clues for exploring the mechanism of caudal fin regeneration. Moreover, we also determined the expressions of inflammatory cytokines IL-1β, IL-6, IL-8, IL-10, TGF-β, and the immune-related pathway JAK2α and STAT1b in the caudal fin of zebrafish following fin amputation by quantitative real time PCR (qPCR). Particularly, Hsp90α expression at mRNA and protein level determined by qPCR and Western blotting, respectively, and whole-mount in situ hybridization of Hsp90α were also performed in this study. The results showed that inflammatory cytokines were mainly expressed in the early period of caudal fin regeneration (1-3 days post amputation, dpa), indicating that fish immune system was involved in the fin regeneration. Furthermore, the high expression of Hsp90α in the vicinity of blastema and blood vessels of the regenerating fin suggests that Hsp90α may play a role in the initiation and promotion of caudal fin regeneration. Overall, our results provide a framework for further understanding the cellular and molecular mechanism in caudal fin regeneration.

Integration of Proteomics and Other Omics Data

In recent biomedical studies, multidimensional profiling, which collects proteomics as well as other types of omics data on the same subjects, is getting increasingly popular. Proteomics, transcriptomics, genomics, epigenomics, and other types of data contain overlapping as well as independent information, which suggests the possibility of integrating multiple types of data to generate more reliable findings/models with better classification/prediction performance. In this chapter, a selective review is conducted on recent data integration techniques for both unsupervised and supervised analysis. The main objective is to provide the "big picture" of data integration that involves proteomics data and discuss the "intuition" beneath the recently developed approaches without invoking too many mathematical details. Potential pitfalls and possible directions for future developments are also discussed.

Generative Adversarial Networks for Robust Breast Cancer Prognosis Prediction with Limited Data Size

Accurate cancer patient prognosis stratification is essential for oncologists to recommend proper treatment plans. Deep learning models are capable of providing good prediction power for such stratification. The main challenge is that only a limited number of labeled patients are available for cancer prognosis. To overcome this, we proposed Wasserstein Generative Adversarial Network-based Deep Adversarial Data Augmentation (wDADA) that leverages generative adversarial networks to perform data augmentation and assist in model training. We used the proposed framework to train our model for predicting disease-specific survival (DSS) of breast cancer patients from the METABRIC dataset. We found that wDADA achieved 0.6726± 0.0278, 0.7538±0.0328, and 0.6507 ±0.0248 in terms of accuracy, AUC, and concordance index in predicting 5-year DSS, respectively, which is comparable to our previously proposed Bimodal model (accuracy: 0.6889±0.0159; AUC: 0.7546± 0.0183; concordance index: 0.6542±0.0120), which needs careful calibration and extensive search on pre-trained network architectures. The flexibility of the proposed wDADA allows us to incorporate it with ensemble learning and semi-supervised learning to further improve performance. Our results indicate that it is possible to utilize generative adversarial networks to train deep models in medical applications, wherein only limited data are available.

Insights into regeneration tool box: An animal model approach

For ages, regeneration has intrigued countless biologists, clinicians, and biomedical engineers. In recent years, significant progress made in identification and characterization of a regeneration tool kit has helped the scientific community to understand the mechanism(s) involved in regeneration across animal kingdom. These mechanistic insights revealed that evolutionarily conserved pathways like Wnt, Notch, Hedgehog, BMP, and JAK/STAT are involved in regeneration. Furthermore, advancement in high throughput screening approaches like transcriptomic analysis followed by proteomic validations have discovered many novel genes, and regeneration specific enhancers that are specific to highly regenerative species like Hydra, Planaria, Newts, and Zebrafish. Since genetic machinery is highly conserved across the animal kingdom, it is possible to engineer these genes and regeneration specific enhancers in species with limited regeneration properties like Drosophila, and mammals. Since these models are highly versatile and genetically tractable, cross-species comparative studies can generate mechanistic insights in regeneration for animals with long gestation periods e.g. Newts. In addition, it will allow extrapolation of regenerative capabilities from highly regenerative species to animals with low regeneration potential, e.g. mammals. In future, these studies, along with advancement in tissue engineering applications, can have strong implications in the field of regenerative medicine and stem cell biology.

Comparative transcriptomic analysis and structure prediction of novel Newt proteins

Notophthalmus viridescens (Red-spotted Newt) possess amazing capabilities to regenerate their organs and other tissues. Previously, using a de novo assembly of the newt transcriptome combined with proteomic validation, our group identified a novel family of five protein members expressed in adult tissues during regeneration in Notophthalmus viridescens. The presence of a putative signal peptide suggests that all these proteins are secretory in nature. Here we employed iterative threading assembly refinement (I-TASSER) server to generate three-dimensional structure of these novel Newt proteins and predicted their function. Our data suggests that these proteins could act as ion transporters, and be involved in redox reaction(s). Due to absence of transgenic approaches in N. viridescens, and conservation of genetic machinery across species, we generated transgenic Drosophila melanogaster to misexpress these genes. Expression of 2775 transcripts were compared between these five newly identified Newt genes. We found that genes involved in the developmental process, cell cycle, apoptosis, and immune response are among those that are highly enriched. To validate the RNA Seq. data, expression of six highly regulated genes were verified using real time Quantitative Polymerase Chain Reaction (RT-qPCR). These graded gene expression patterns provide insight into the function of novel protein family identified in Newt, and layout a map for future studies in the field.

A Reliable Preclinical Model to Study the Impact of Cigarette Smoke in Development and Disease

The World Health Organization has estimated that, worldwide, cigarette smoking has caused more than 100 million deaths in the last century, a number that is expected to increase in the future. Understanding cigarette smoke toxicity is key for research and development of proper public health policies. The current challenge is to establish a reliable preclinical model to evaluate the effects of cigarette smoke. In this work, we describe a simple method that allows for quantifying the toxic effects of cigarette smoke using zebrafish. Here, viability of larvae and adult fish, as well as the effects of cigarette smoke extracts on vascular development and tissue regeneration, can be easily assayed. © 2019 by John Wiley & Sons, Inc.