Cell and small animal models for phenotypic drug discovery

The Prodrug DHED Delivers 17β-Estradiol into the Retina for Protection of Retinal Ganglion Cells and Preservation of Visual Function in an Animal Model of Glaucoma

We report a three-pronged phenotypic evaluation of the bioprecursor prodrug 10β,17β-dihydroxyestra-1,4-dien-3-one (DHED) that selectively produces 17β-estradiol (E2) in the retina after topical administration and halts glaucomatous neurodegeneration in a male rat model of the disease. Ocular hypertension (OHT) was induced by hyperosmotic saline injection into an episcleral vein of the eye. Animals received daily DHED eye drops for 12 weeks. Deterioration of visual acuity and contrast sensitivity by OHT in these animals were markedly prevented by the DHED-derived E2 with concomitant preservation of retinal ganglion cells and their axons. In addition, we utilized targeted retina proteomics and a previously established panel of proteins as preclinical biomarkers in the context of OHT-induced neurodegeneration as a characteristic process of the disease. The prodrug treatment provided retina-targeted remediation against the glaucomatous dysregulations of these surrogate endpoints without increasing circulating E2 levels. Collectively, the demonstrated significant neuroprotective effect by the DHED-derived E2 in the selected animal model of glaucoma supports the translational potential of our presented ocular neuroprotective approach owing to its inherent therapeutic safety and efficacy.

Microfluidics in High-Throughput Drug Screening: Organ-on-a-Chip and C. elegans-Based Innovations

The development of therapeutic interventions for diseases necessitates a crucial step known as drug screening, wherein potential substances with medicinal properties are rigorously evaluated. This process has undergone a transformative evolution, driven by the imperative need for more efficient, rapid, and high-throughput screening platforms. Among these, microfluidic systems have emerged as the epitome of efficiency, enabling the screening of drug candidates with unprecedented speed and minimal sample consumption. This review paper explores the cutting-edge landscape of microfluidic-based drug screening platforms, with a specific emphasis on two pioneering approaches: organ-on-a-chip and C. elegans-based chips. Organ-on-a-chip technology harnesses human-derived cells to recreate the physiological functions of human organs, offering an invaluable tool for assessing drug efficacy and toxicity. In parallel, C. elegans-based chips, boasting up to 60% genetic homology with humans and a remarkable affinity for microfluidic systems, have proven to be robust models for drug screening. Our comprehensive review endeavors to provide readers with a profound understanding of the fundamental principles, advantages, and challenges associated with these innovative drug screening platforms. We delve into the latest breakthroughs and practical applications in this burgeoning field, illuminating the pivotal role these platforms play in expediting drug discovery and development. Furthermore, we engage in a forward-looking discussion to delineate the future directions and untapped potential inherent in these transformative technologies. Through this review, we aim to contribute to the collective knowledge base in the realm of drug screening, providing valuable insights to researchers, clinicians, and stakeholders alike. We invite readers to embark on a journey into the realm of microfluidic-based drug screening platforms, fostering a deeper appreciation for their significance and promising avenues yet to be explored.

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

Mucopolysaccharidosis IVA: Current Disease Models and Drawbacks

Mucopolysaccharidosis IVA (MPS IVA) is a rare disorder caused by mutations in the N-acetylgalactosamine-6-sulfate-sulfatase (GALNS) encoding gene. GALNS leads to the lysosomal degradation of the glycosaminoglyccreasans keratan sulfate and chondroitin 6-sulfate. Impaired GALNS enzymes result in skeletal and non-skeletal complications in patients. For years, the MPS IVA pathogenesis and the assessment of promising drugs have been evaluated using in vitro (primarily fibroblasts) and in vivo (mainly mouse) models. Even though value information has been raised from those studies, these models have several limitations. For instance, chondrocytes have been well recognized as primary cells affected in MPS IVA and responsible for displaying bone development impairment in MPS IVA patients; nonetheless, only a few investigations have used those cells to evaluate basic and applied concepts. Likewise, current animal models are extensively represented by mice lacking GALNS expression; however, it is well known that MPS IVA mice do not recapitulate the skeletal dysplasia observed in humans, making some comparisons difficult. This manuscript reviews the current in vitro and in vivo MPS IVA models and their drawbacks.

Human Lung Organoids-A Novel Experimental and Precision Medicine Approach

The global burden of respiratory diseases is very high and still on the rise, prompting the need for accurate models for basic and translational research. Several model systems are currently available ranging from simple airway cell cultures to complex tissue-engineered lungs. In recent years, human lung organoids have been established as highly transferrable three-dimensional in vitro model systems for lung research. For acute infectious and chronic inflammatory diseases as well as lung cancer, human lung organoids have opened possibilities for precise in vitro research and a deeper understanding of mechanisms underlying lung injury and regeneration. Human lung organoids from induced pluripotent stem cells or from adult stem cells of patients' samples introduce tools for understanding developmental processes and personalized medicine approaches. When further state-of-the-art technologies and protocols come into use, the full potential of human lung organoids can be harnessed. High-throughput assays in drug development, gene therapy, and organoid transplantation are current applications of organoids in translational research. In this review, we emphasize novel approaches in translational and personalized medicine in lung research focusing on the use of human lung organoids.

Rewiring Drug Research and Development through Human Data-Driven Discovery (HD3)

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD³) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD³, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

Entrectinib, a new multi-target inhibitor for cancer therapy

Clinical practice shows that when single-target drugs treat multi-factor diseases such as tumors, cardiovascular system and endocrine system diseases, it is often difficult to achieve good therapeutic effects, and even serious adverse reactions may occur. Multi-target drugs can simultaneously regulate multiple links of disease, improve efficacy, reduce adverse reactions, and improve drug resistance. They are ideal drugs for treating complex diseases, and therefore have become the main direction of drug development. At present, some multi-target drugs have been successfully used in many major diseases. Entrectinib is an oral small molecule inhibitor that targets TRK, ROS1, and ALK. It is used to treat locally advanced or metastatic solid tumors with NTRK1/2/3, ROS1 and ALK gene fusion mutations. It can pass through the blood-brain barrier and is the only TRK inhibitor clinically proven to be effective against primary and metastatic brain diseases. In 2019, entrectinib was approved by the FDA to treat adult patients with ROS1-positive metastatic non-small cell lung cancer. Case reports showed that continuous administration of entrectinib was effective and tolerable. In this review, we give a brief introduction to TKK, ROS1 and ALK, and on this basis, we give a detailed and comprehensive introduction to the mechanism of action, pharmacokinetics, pharmacodynamics, clinical efficacy, tolerability and drug interactions of entrectinib.

Accelerating reaction generality and mechanistic insight through additive mapping

Reaction generality is crucial in determining the overall impact and usefulness of synthetic methods. Typical generalization protocols require a priori mechanistic understanding and suffer when applied to complex, less understood systems. We developed an additive mapping approach that rapidly expands the utility of synthetic methods while generating concurrent mechanistic insight. Validation of this approach on the metallaphotoredox decarboxylative arylation resulted in the discovery of a phthalimide ligand additive that overcomes many lingering limitations of this reaction and has important mechanistic implications for nickel-catalyzed cross-couplings.

Human Protein Complex-Based Drug Signatures for Personalized Cancer Medicine

Disease signature-based drug repositioning approaches typically first identify a disease signature from gene expression profiles of disease samples to represent a particular disease. Then such a disease signature is connected with the drug-induced gene expression profiles to find potential drugs for the particular disease. In order to obtain reliable disease signatures, the size of disease samples should be large enough, which is not always a single case in practice, especially for personalized medicine. On the other hand, the sample sizes of drug-induced gene expression profiles are generally large. In this study, we propose a new drug repositioning approach (HDgS), in which the drug signature is first identified from drug-induced gene expression profiles, and then connected to the gene expression profiles of disease samples to find the potential drugs for patients. In order to take the dependencies among genes into account, the human protein complexes (HPC) are used to define the drug signature. The proposed HDgS is applied to the drug-induced gene expression profiles in LINCS and several types of cancer samples. The results indicate that the HPC-based drug signature can effectively find drug candidates for patients and that the proposed HDgS can be applied for personalized medicine with even one patient sample.

A zebrafish screen reveals Renin-angiotensin system inhibitors as neuroprotective via mitochondrial restoration in dopamine neurons

Parkinson’s disease (PD) is a common neurodegenerative disorder without effective disease-modifying therapeutics. Here, we establish a chemogenetic dopamine (DA) neuron ablation model in larval zebrafish with mitochondrial dysfunction and robustness suitable for high-content screening. We use this system to conduct an in vivo DA neuron imaging-based chemical screen and identify the Renin-Angiotensin-Aldosterone System (RAAS) inhibitors as significantly neuroprotective. Knockdown of the angiotensin receptor 1 (agtr1) in DA neurons reveals a cell-autonomous mechanism of neuroprotection. DA neuron-specific RNA-seq identifies mitochondrial pathway gene expression that is significantly restored by RAAS inhibitor treatment. The neuroprotective effect of RAAS inhibitors is further observed in a zebrafish Gaucher disease model and Drosophila pink1-deficient PD model. Finally, examination of clinical data reveals a significant effect of RAAS inhibitors in delaying PD progression. Our findings reveal the therapeutic potential and mechanisms of targeting the RAAS pathway for neuroprotection and demonstrate a salient approach that bridges basic science to translational medicine.

Parkinson’s disease is caused by the slow death and deterioration of brain cells, in particular of the neurons that produce a chemical messenger known as dopamine. Certain drugs can mitigate the resulting drop in dopamine levels and help to manage symptoms, but they cause dangerous side-effects. There is no treatment that can slow down or halt the progress of the condition, which affects 0.3% of the population globally.

Many factors, both genetic and environmental, contribute to the emergence of Parkinson’s disease. For example, dysfunction of the mitochondria, the internal structures that power up cells, is a known mechanism associated with the death of dopamine-producing neurons.

Zebrafish are tiny fish which can be used to study Parkinson’s disease, as they are easy to manipulate in the lab and share many characteristics with humans. In particular, they can be helpful to test the effects of various potential drugs on the condition.

Here, Kim et al. established a new zebrafish model in which dopamine-producing brain cells die due to their mitochondria not working properly; they then used this assay to assess the impact of 1,403 different chemicals on the integrity of these cells. A group of molecules called renin-angiotensin-aldosterone (RAAS) inhibitors was shown to protect dopamine-producing neurons and stopped them from dying as often. These are already used to treat high blood pressure as they help to dilate blood vessels. In the brain, however, RAAS worked by restoring certain mitochondrial processes.

Kim et al. then investigated whether these results are relevant in other, broader contexts. They were able to show that RAAS inhibitors have the same effect in other animals, and that Parkinson’s disease often progresses more slowly in patients that already take these drugs for high blood pressure.

Taken together, these findings therefore suggest that RAAS inhibitors may be useful to treat Parkinson’s disease, as well as other brain illnesses that emerge because of mitochondria not working properly. Clinical studies and new ways to improve these drugs are needed to further investigate and capitalize on these potential benefits.

Beyond Pseudo-natural Products: Sequential Ugi/Pictet-Spengler Reactions Leading to Steroidal Pyrazinoisoquinolines That Trigger Caspase-Independent Death in HepG2 Cells

In this work, we describe how stereochemically complex polycyclic compounds can be generated by applying a synthetic sequence comprising an intramolecular Ugi reaction followed by a Pictet-Spengler cyclization on steroid-derived scaffolds. The resulting compounds, which combine a fragment derived from a natural product and a scaffold not found in nature. are both structurally distinct and globally similar to natural products at the same time, and interrogate an alternative region of the chemical space. One of the new compounds showed significant antiproliferative activity on HepG2 cells through a caspase-independent cell-death mechanism, an appealing feature when new antitumor compounds are searched.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no "one size fits all" approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

Human pluripotent stem cell-derived lung organoids: Potential applications in development and disease modeling

The pulmonary system is comprised of two main compartments, airways and alveolar space. Their tissue and cellular complexity ensure lung function and protection from external agents, for example, virus. Two-dimensional (2D) in vitro systems and animal models have been largely employed to elucidate the molecular mechanisms underlying human lung development, physiology, and pathogenesis. However, neither of these models accurately recapitulate the human lung environment and cellular crosstalk. More recently, human-derived three-dimensional (3D) models have been generated allowing for a deeper understanding of cell-to-cell communication. However, the availability and accessibility of primary human cell sources from which generate the 2D and 3D models may be limited. In the past few years, protocols have been developed to successfully employ human pluripotent stem cells (hPSCs) and differentiate them toward pulmonary fate in vitro. In the present review, we discuss the advantages and pitfalls of hPSC-derived lung 2D and 3D models, including the main characteristics and potentials for these models and their current and future applications for modeling development and diseases. Lung organoids currently represent the closest model to the human pulmonary system. We further focus on the applications of lung organoids for the study of human diseases such as pulmonary fibrosis, infectious diseases, and lung cancer. Finally, we discuss the present limitations and potential future applications of 3D lung organoids. This article is categorized under: Adult Stem Cells, Tissue Renewal, and Regeneration > Stem Cells and Disease Adult Stem Cells, Tissue Renewal, and Regeneration > Stem Cell Differentiation and Reversion.

The Resurrection of Phenotypic Drug Discovery

Prior to genetic mapping, the majority of drug discovery efforts involved phenotypic screening, wherein compounds were screened in either in vitro or in vivo models thought to mimic the disease state of interest. While never completely abandoning phenotypic approaches, the labor intensive nature of such tests encouraged the pharmaceutical industry to move away from them in favor of target-based drug discovery, which facilitated throughput and allowed for the efficient screening of large numbers of compounds. However, a consequence of reliance on target-based screening was an increased number of failures in clinical trials due to poor correlation between novel mechanistic targets and the actual disease state. As a result, the field has seen a recent resurrection in phenotypic drug discovery approaches. In this work, we highlight some recent phenotypic projects from our industrial past and in our current academic drug discovery environment that have provided encouraging results.

Drug Repurposing Approaches: Existing Leads For Novel Threats And Drug Targets

Drug Repurposing (DR) is an alternative to the traditional drug discovery process. It is cost and time effective, with high returns and low risk process that can tackle the increasing need for interventions for varied diseases and new outbreaks. Repurposing of old drugs for other diseases has gained a wider attention, as there have been several old drugs approved by FDA for new diseases. In the global emergency of COVID19 pandemic, this is one of the strategies implemented in repurposing of old anti-infective, anti-rheumatic and anti-thrombotic drugs. The goal of the current review is to elaborate the process of DR, its advantages, repurposed drugs for a plethora of disorders, and the evolution of related academic publications. Further, detailed are the computational approaches: literature mining and semantic inference, network-based drug repositioning, signature matching, retrospective clinical analysis, molecular docking and experimental phenotypic screening. We discuss the legal and economical potential barriers in DR, existent collaborative models and recommendations for overcoming these hurdles and leveraging the complete potential of DR in finding new indications.

Phenotypic Screening in C. elegans as a Tool for the Discovery of New Geroprotective Drugs

Population aging is one of the largest challenges of the 21st century. As more people live to advanced ages, the prevalence of age-related diseases and disabilities will increase placing an ever larger burden on our healthcare system. A potential solution to this conundrum is to develop treatments that prevent, delay or reduce the severity of age-related diseases by decreasing the rate of the aging process. This ambition has been accomplished in model organisms through dietary, genetic and pharmacological interventions. The pharmacological approaches hold the greatest opportunity for successful translation to the clinic. The discovery of such pharmacological interventions in aging requires high-throughput screening strategies. However, the majority of screens performed for geroprotective drugs in C. elegans so far are rather low throughput. Therefore, the development of high-throughput screening strategies is of utmost importance.

Duchenne muscular dystrophy hiPSC-derived myoblast drug screen identifies compounds that ameliorate disease in mdx mice

Duchenne muscular dystrophy (DMD) is the most common muscular dystrophy. In the present study, when human induced pluripotent stem cells (hiPSCs) were differentiated into myoblasts, the myoblasts derived from DMD patient hiPSCs (DMD hiPSC-derived myoblasts) exhibited an identifiable DMD-relevant phenotype: myogenic fusion deficiency. Based on this model, we developed a DMD hiPSC-derived myoblast screening platform employing a high-content imaging (BD Pathway 855) approach to generate parameters describing morphological as well as myogenic marker protein expression. Following treatment of the cells with 1524 compounds from the Johns Hopkins Clinical Compound Library, compounds that enhanced myogenic fusion of DMD hiPSC-derived myoblasts were identified. The final hits were ginsenoside Rd and fenofibrate. Transcriptional profiling revealed that ginsenoside Rd is functionally related to FLT3 signaling, while fenofibrate is linked to TGF-β signaling. Preclinical tests in mdx mice showed that treatment with these 2 hit compounds can significantly ameliorate some of the skeletal muscle phenotypes caused by dystrophin deficiency, supporting their therapeutic potential. Further study revealed that fenofibrate could inhibit mitochondrion-induced apoptosis in DMD hiPSC-derived cardiomyocytes. We have developed a platform based on DMD hiPSC-derived myoblasts for drug screening and identified 2 promising small molecules with in vivo efficacy.

Activity-based protein profiling: Recent advances in medicinal chemistry

Activity-based protein profiling (ABPP) has become an emerging chemical proteomic approach to illustrate the interaction mechanisms between compounds and proteins. This approach has combined organic synthesis, biochemistry, cell biology, biophysics and bioinformatics to accelerate the process of drug discovery in target identification and validation, as well as in the stage of lead discovery and optimization. This review will summarize new developments and applications of ABPP in medicinal chemistry. Here, we mainly described the design principles of activity-base probes (ABPs) and general workflows of ABPP approach. Moreover, we discussed various basic and advanced ABPP strategies and their applications in medicinal chemistry, including competitive and comparative ABPP, two-step ABPP, fluorescence polarization ABPP (FluoPol-ABPP) and ABPs for visualization. In conclusion, this review will give a general overview of the applications of ABPP as a powerful and efficient technique in medicinal chemistry.

Prospects for incorporation of epigenetic biomarkers in human health and environmental risk assessment of chemicals

Epigenetic mechanisms have gained relevance in human health and environmental studies, due to their pivotal role in disease, gene × environment interactions and adaptation to environmental change and/or contamination. Epigenetic mechanisms are highly responsive to external stimuli and a wide range of chemicals has been shown to determine specific epigenetic patterns in several organisms. Furthermore, the mitotic/meiotic inheritance of such epigenetic marks as well as the resulting changes in gene expression and cell/organismal phenotypes has now been demonstrated. Therefore, epigenetic signatures are interesting candidates for linking environmental exposures to disease as well as informing on past exposures to stressors. Accordingly, epigenetic biomarkers could be useful tools in both prospective and retrospective risk assessment but epigenetic endpoints are currently not yet incorporated into risk assessments. Achieving a better understanding on this apparent impasse, as well as identifying routes to promote the application of epigenetic biomarkers within environmental risk assessment frameworks are the objectives of this review. We first compile evidence from human health studies supporting the use of epigenetic exposure-associated changes as reliable biomarkers of exposure. Then, specifically focusing on environmental science, we examine the potential and challenges of developing epigenetic biomarkers for environmental fields, and discuss useful organisms and appropriate sequencing techniques to foster their development in this context. Finally, we discuss the practical incorporation of epigenetic biomarkers in the environmental risk assessment of chemicals, highlighting critical data gaps and making key recommendations for future research within a regulatory context.

Microfluidic devices for embryonic and larval zebrafish studies

Zebrafish or Danio rerio is an established model organism for studying the genetic, neuronal and behavioral bases of diseases and for toxicology and drug screening. The embryonic and larval stages of zebrafish have been used extensively in fundamental and applied research due to advantages offered such as body transparency, small size, low cost of cultivation and high genetic homology with humans. However, the manual experimental methods used for handling and investigating this organism are limited due to their low throughput, labor intensiveness and inaccuracy in delivering external stimuli to the zebrafish while quantifying various neuronal and behavioral responses. Microfluidic and lab-on-a-chip devices have emerged as ideal technologies to overcome these challenges. In this review paper, the current microfluidic approaches for investigation of behavior and neurobiology of zebrafish at embryonic and larval stages will be reviewed. Our focus will be to provide an overview of the microfluidic methods used to manipulate (deliver and orient), immobilize and expose or inject zebrafish embryos or larvae, followed by quantification of their responses in terms of neuron activities and movement. We will also provide our opinion in terms of the direction that the field of zebrafish microfluidics is heading toward in the area of biomedical engineering.

Emerging Drug Therapies for Inherited Retinal Dystrophies

Worldwide, 1 in 2000 people suffer from inherited retinal dystrophies (IRD). Individuals with IRD typically present with progressive vision loss that ultimately results in blindness. Unfortunately, effective treatment options are not widely available due to the genetic and clinical heterogeneity of these diseases. There are multiple gene, cell, and drug-based therapies in various phases of clinical trials for IRD. This mini-review documents current progress made in drug-based clinical trials for treating IRD.

Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes

BACKGROUND

Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments.

RESULTS

Autoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL (feed forward artificial neural network with three hidden layers) were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics.

CONCLUSION

Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli.

Phenotypic Screen Identifies a Small Molecule Modulating ERK2 and Promoting Stem Cell Proliferation

Stem cells display a fundamentally different mechanism of proliferation control when compared to somatic cells. Uncovering these mechanisms would maximize the impact in drug discovery with a higher translational applicability. The unbiased approach used in phenotype-based drug discovery (PDD) programs can offer a unique opportunity to identify such novel biological phenomenon. Here, we describe an integrated phenotypic screening approach, employing a combination of in vitro and in vivo PDD models to identify a small molecule increasing stem cell proliferation. We demonstrate that a combination of both in vitro and in vivo screening models improves hit identification and reproducibility of effects across various PDD models. Using cell viability and colony size phenotype measurement we characterize the structure activity relationship of the lead molecule, and identify that the small molecule inhibits phosphorylation of ERK2 and promotes stem cell proliferation. This study demonstrates a PDD approach that employs combinatorial models to identify compounds promoting stem cell proliferation.