Since its initial approval in 1989 for the treatment of blepharospasm and other facial spasms, BoNT-A has evolved into a therapeutic modality for a variety of neurological and non-neurological disorders.  BoNT-A has been reported to be effective for the treatment of cervical dystonia, bruxism, tremors, tics, myoclonus, restless legs syndrome, tardive dyskinesia, spasticity, chronic migraines, hypersalivation, spinal cord injuries and a variety of symptoms associated with Parkinson’s disease. Research is ongoing for potential CNS applications in the treatment of neurodegenerative disorders.²

In addition to neurological applications, BoNT-A is also used in aesthetics to relax muscles and smooth the upper facial lines such as glabellar lines, forehead wrinkles and crow’s feet.

Treatment Length

Treatments have an onset of a few days with a peak effect two weeks after injection. Although treatment duration is 3-4 months, the effect decreases progressively after 1-2 months necessitating regular injections. This is especially relevant in therapeutics indications. When symptoms reoccur they impact quality of life and the ability to function.

“A key goal is to improve toxin performance, and, notably, to increase the duration,” says Mickael Machicoane, PhD, VP Research at Fastox Pharma. “An easy way to achieve this is to inject higher doses; however, a plateau effect is reached quickly. The increase in duration is not proportional to the injected amount.”

Improvement in Durability

Lausanne-based Fastox has developed a lead candidate, FTP-501, that speeds up onset and allows the treatment to reach a higher response and greater durations of 50%-100% when injected in combination with BoNT-A. Experimental data show that at each time point the response is higher with the combination than with the toxin alone.

Results have been demonstrated in two recognized animal model tests – the digit abduction score (DAS) and the free running wheel – using virtually all BoNT-A formulations. “FTP-501 enhances the key toxins on the market today including Botox^®, Dysport^®, Xeomin^® and Alluzience^®, the new liquid toxin in Europe,” says Machicoane.

The typical BoNT-A toxin, which has a slow onset and a moderate duration, is combined with the booster FTP-501, which acts postsynaptically to inhibit the muscle contraction, and potentiate the action of the toxin. “We call this molecule a postsynaptic inhibitor (POSI),” explains Machicoane. “Our patented LAST technology is a new paradigm. To our knowledge, we are the only company doing postsynaptic investigations. Other companies focus on trying to improve the presynaptic behavior with toxin engineering or formulation with less success.”

Mechanism of Action (MOA)

To perform its inhibitory function the BoNT needs to enter the motoneuron. It does so by hijacking the natural exo/endocytic machinery of the neuron, which releases and recycles neurotransmitters in the synaptic cleft. BoNT uses these vesicles as entry doors into the motoneuron.

“The addition of POSI first rapidly blocks the muscle,” says Machicoane. “This blockade is sensed by the motoneuron that gets frustrated because of the absence of response in the muscle and increases its release of neurotransmitters. This increase in exocytosis allows more BoNT to enter the neuron quicker leading to faster onset and longer duration. We plan to enter clinical trials later this year.”

Fastox collaborates with the team of Ornella Rossetto, PhD, associate professor, and Marco Pirazzini, PhD, assistant professor, at the Neuroparalysis and Neuroregeneration Laboratory of the University of Padova. These experts have pioneered the discovery of fundamental mechanisms of clostridium toxins and developed tools to monitor the activity of BoNT within motoneurons that permit the exploration of the MOA of the combination of BoNT with POSI.

It was previously known that BoNT-A toxin performs its inhibitory function by cleaving SNAP-25, a key component for the neuron to release neurotransmitters for signaling to the muscle. This collaborative work has shown that the combination of BoNT-A and POSI enables the cleavage of SNAP-25 inside the neuron more quickly than with the toxin alone.

Currently focusing on FTP-501, their lead candidate, Fastox has a pipeline of other candidates.

References

1. Rocka A, Piędel F, Jasielski PP, Piwek M, Petit V, Rejdak K. Botulinum in Selected Neurological Disorders – Review. Journal of Pre-Clinical and Clinical Research 2021, Vol 15, No 4, 176-183.
2. Anandan, C and Jankovic, J. Botulinum Toxin in Movement Disorders: An Update. Toxins 2021, 13, 42.

The post Extending Botulinum Treatments appeared first on GEN - Genetic Engineering and Biotechnology News.

Post-translational modifications (PTMs) provide a rapid mechanism that enables protein phenotypic diversity so that proteins can react to external and internal disturbances and regulate cellular activity. Advanced mass spectrometry (MS) analysis has allowed for the identification of over 600 distinct PTM classes jointly comprising an order of 10⁶ unique sites but the true functional fraction is unknown.¹ Major PTM types include phosphorylation, acetylation, glycosylation, succinylation, methylation, malonylation, SUMOylation, and ubiquitination.²

Discoveries continue

The focus of vertebrate studies has mainly been on canonical phosphorylation, but research indicates that phosphorylation of other non-canonical amino acids also regulates integral aspects of cell biology. According to Cristina Martin-Granados, PhD, cell signaling research area scientific lead at Abcam, Claire Eyers used strong anion exchange-mediated phosphoproteomics to detect non-canonical phosphorylation. The findings indicated that non-canonical phospho-sites account for approximately one-third of the number of observed canonical phospho-sites.³

Non-canonical phosphorylation is highly susceptible to hydrolysis at low pH and/or at elevated temperature, therefore, standard biochemical techniques of phosphoprotein characterization are largely unsuitable for analysis of “atypical” phosphorylated amino acids.

In addition, the discovery of non-lysine ubiquitylation led to the concomitant revelation that non-proteinaceous ubiquitylation substrates such as glycogen exist.⁴ “This represents a major paradigm shift in our understanding of ubiquitylation and the breadth of biological processes that it regulates,” said Martin-Granados. Over a decade ago, reports highlighting the existence and biological relevance of non-lysine ubiquitylation first appeared.⁵ The number of studies has since rocketed.^6-11

Technical challenges

“PTMs are sub-stoichiometric, highly dynamic, transient, and generally labile in nature,” said Martin-Granados. “They are often present in a small subfraction of the protein population making detection by antibody-based approaches difficult.”

Enrichment of a specific PTM can help tackle low stoichiometry challenges. For example, immunoprecipitation can be performed before Western blotting or MS analysis. Ion exchange, immobilized metal ion affinity, and immunoaffinity chromatography are also enrichment techniques that can be used to segregate PTM proteins/peptides from the unmodified pools, to decrease sample heterogenicity while increasing analytic efficiency and reliability.

PTMs can be cell- or tissue-specific and experiments require very stringent controls. The choice of positive and negative experimental controls is essential to correctly interpreting results. “There is evidence that some PTM modifications can block the binding site of the antibody on its target protein leading to a false negative result,” said Martin-Granados. In addition, since many PTMs are the aftermath of enzymatic reactions, sample processing can affect the target if unwanted enzymatic activity is not controlled.

Characterization of PTMs relies heavily on proteomics analyses. Antibodies are essential detection and enrichment tools. “But the development of highly specific antibodies with exquisite binding affinity remains challenging due to the small size of the PTM chemical moieties, similarities in the chemical structure, and poor antigenicity. In addition, specific recognition of the PTM and surrounding sequence or a pan-PTM may be required,” said Martin-Granados. Polycloncal antibodies can present drawbacks in delivering reproducible and reliable data due to strong lot-to-lot variations.

Useful tools

Computational methods for predicting PTMs are attracting considerable attention. The AI program AlphaFold is a valuable tool to predict unsolved protein structures from their amino-acid sequence.^12,13

Unfortunately, AlphaFold2 does not consider the impact of PTMs on protein structure, but databases on protein PTMs and computational tools are available, and NMR spectroscopy and MS largely complement the limitations of AlphaFold2.^14,15 With time, it may become possible to create and integrate new algorithms into AlphaFold2-generated structures and PTM databases to achieve a comprehensive outlook for PTM prediction.¹⁵

Although advances in MS have enabled the mapping of individual ubiquitin modifications that generate the ubiquitin code, the intricate architecture of polyubiquitin signals has remained largely elusive. Ubiquitin-clipping is a novel methodology that has provided insight into ubiquitin chain architecture and can be useful to decipher combinatorial complexity and architecture.¹⁶

The discovery of ester-linked ubiquitin linkages also presents an opportunity to design new antibodies against these linkages. “This will be challenging due to the increased ability of the ester bond when compared to the canonical isopeptide linkage,” said Martin-Granados.

Another recent strategy is specific uncaging-assisted biotinylation and mapping of phosphoproteome, SubMAPP, which integrates an activatable proximity labeling enzyme with an orthogonal phosphorylation enrichment scheme and LC-MS/MS. SubMAPP is a highly sensitive method to characterize the subcellular phosphoproteome in living systems with high temporal resolution.¹⁷

References

1. Bradley D. The evolution of post-translational modifications. Curr Opin Genet Dev. 2022 Oct;76:101956.  doi: 10.1016/j.gde.2022.101956. Epub 2022 Jul 14.
2. Ramazi S and Zahiri J. Post-translational modifications in proteins: resources, tools and prediction. Database, Volume 2021, 2021, baab012. doi: 10.1093/database/baab012
3. Hardman G, et al. Strong anion exchange-mediated phosphoproteomics reveals extensive human non-canonical phosphorylation. The EMBO Journal (2019)38:e100847. doi:10.15252/embj.2018100847
4. Kelsall IR, et al. HOIL-1 ubiquitin ligase activity targets unbranched glucosaccharides and is required to prevent polyglucosan accumulation. The EMBO Journal (2022)41:e10970. doi:10.15252/embj.2021109700
5. Wang X, et al. Ube2j2 ubiquitinates hydroxylated amino acids on ER-associated degradation substrates. J Cell Biol (2009) 187 (5): 655–668. doi:10.1083/jcb.200908036
6. Pao KC, et al. Activity-based E3 ligase profiling uncovers an E3 ligase with esterification activity. Nature. 2018 Apr;556(7701):381-385. doi: 10.1038/s41586-018-0026-1. Epub 2018 Apr 11. PMID: 29643511
7. Kelsall IR, et al. The E3 ligase HOIL-1 catalyses ester bond formation between ubiquitin and components of the Myddosome in mammalian cells. PNAS. 116 (27) 13293-13298. doi:10.1073/pnas.1905873116
8. Otten EG, et al. Ubiquitylation of lipopolysaccharide by RNF213 during bacterial infection. Nature. 2021 Jun;594(7861):111-116. doi: 10.1038/s41586-021-03566-4. Epub 2021 May 19. PMID: 34012115; PMCID: PMC7610904
9. Zhu K, et al. DELTEX E3 ligases ubiquitylate ADP-ribosyl modification on protein substrates. Science Advances, 2022, 8 (40), ff10.1126/sciadv.add4253ff. ffhal-03862525f
10. Sakamaki J and Mizushima N. Protocol to purify and detect ubiquitinated phospholipids in budding yeast and human cell lines. STAR Protocols, Volume 4, Issue 1,2023, 101935, ISSN 2666-1667, doi:10.1016/j.xpro.2022.101935
11. McCrory EH, Akimov V, Cohen P, Blagoev B. Identification of ester-linked ubiquitylation sites during TLR7 signalling increases the number of inter-ubiquitin linkages from 8 to 12. Biochem J 9 December 2022; 479 (23): 2419–2431. doi:10.1042/BCJ20220510
12. Jumper J, et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). doi:10.1038/s41586-021-03819-2
13. Varadi M, et al. Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D439–D444, doi:10.1093/nar/gkab1061
14. Laurents DV. AlphaFold 2 and NMR Spectroscopy: Partners to Understand Protein Structure, Dynamics and Function. Frontiers in Molecular Biosciences. Vol 9 2022. doi:10.3389/fmolb.2022.906437  
15. Biehn SE and Lindert S. Protein Structure Prediction with Mass Spectrometry Data. Annual Review of Physical Chemistry 2022 73:1, 1-19. doi:10.1146/annurev-physchem-082720-123928
16. Swatek KN, et al. Insights into ubiquitin chain architecture using Ub-clipping. Nature. 2019 Aug;572(7770):533-537. doi: 10.1038/s41586-019-1482-y. Epub 2019 Aug 15. PMID: 31413367; PMCID: PMC6823057
17. Liu Y, et al. Spatiotemporally resolved subcellular phosphoproteomics. 2021 PNAS 118, e2025299118, doi:10.1073/pnas.2025299118

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CS has been shown to play an important role in severe COVID-19 cases and reported as a major cause of death.² Pathogenesis heightens with immune dysregulation that leads to uncontrolled multisystem inflammatory response, caused by overproduction of pro-inflammatory cytokines.³ The increase in inflammatory mediators is correlated with a reduction of innate and adaptive cytotoxic antiviral function. Monitoring and accurate knowledge regarding clinical worsening is crucial to choosing appropriate interventions aimed to reduce mortality.⁴

Real-time cytokine measurement

An approach to monitoring cytokines is Ella, a fully-automated, microfluidic platform that supports multiplex sandwich immunoassays. “Simply add your samples and wash buffer and then let it run. Ella automates all aspects of the assay run and provides fully analyzed results in 90 minutes,” said Steve Crouse, senior vp & GM ASD at Bio-Techne. “Ella provides triplicate results for each sample and minimizes pipetting by offering a factory-generated standard curve.”

All Bio-Techne CS assays have coefficients of variation (CVs) below 10%, which leads to consistency when Ella is used as a distributed platform. “We have demonstrated that the performance of the sample on any of our lots of CS plates used on any of our instruments will be the same,” said Crouse. “Consistent data are important for monitoring a patient over time as well as comparing results from different locations.”

Bio-Techne collaborated with Mount Sinai’s Human Immune Monitoring Center, led by Miriam Merad, MD, PhD, Mount Sinai endowed professor in cancer immunology, and Sacha Gnajtic, PhD, associate professor of immunology and hematology/oncology, to identify cytokines that produced the most adverse response in a COVID-19 infection and to develop the CS test.⁵

The 4-plex CS panel designed for COVID-19 quantifies IL-lβ, IL-6, IL-8, and TNFα. “Along with fixed panels like our CS panel, we offer numerous cytokine and chemokine assays that can be combined in different ways up to an 8-plex to support different immune monitoring strategies,” said Crouse.

The COVID-19 pandemic produced the most research on the role of CS and the importance of monitoring cytokines to adjust treatment in severely ill patients. “This is starting to transition,” said Crouse. “Cell therapy is probably the largest driver, but other disease states or therapeutic approaches also require monitoring. However, to use monitoring broadly as a standard of care, it needs to clearly show that results can lead to a specific clinical intervention or outcome. This is not yet well defined in most instances but current research efforts point to immune monitoring becoming more prevalent over the next couple of years.”

Ella is a broad-based immunoassay platform with over 200 available markers and is used in a range of applications such as clinical trials for new drugs since it can be distributed to multiple clinical research organizations (CROs) for global trials. In cell therapy, it can also be used to look at interferon-γ as an indicator of T cell activation, for QC release of the cell therapy, in addition to patient monitoring. “From every instrument and every lot of assays you can trust that data are consistent, reproducible, and comparable over long periods and across multiple sites,” said Crouse.

Other approaches to monitoring

Immunoassays or ELISAs are typically used to measure cytokines. Other sandwich immunoassays also allow for cytokine quantification. “It is hard to achieve the same level of data precision as the 90-minute Ella CS assay while still maintaining sensitivity. A very sensitive platform is imperative for the detection of endogenous cytokine levels,” said Crouse.

A University of Michigan team also wanted to address the gap in monitoring cytokines in severely ill COVID-19 patients and reported on a microfluidic digital immunoassay platform under development that enables rapid 4-plex measurement of cytokines in COVID-19 patient serum. The assay employs single-molecule counting for an antibody sandwich immune-complex formation quenched at an early pre-equilibrium state resulting in a detection limit < 0.4 pg/mL and a linear dynamic range of 10³ while requiring an assay incubation time as short as 9 min.⁶

References

1. Jarczak D, Nierhaus A. Cytokine Storm—Definition, Causes, and Implications. J. Mol. Sci. 2022, 23, 11740. doi: 10.3390/ijms231911740
2. Mandel M, Harari G, Gurevich M, Achiron A. Cytokine Prediction of Mortality in COVID-19 Patients, Cytokine. 2020 Oct;134:155190. doi: 10.1016/j.cyto.2020.155190. Epub 2020 Jul 10.
3. Shcherbak SG, Anisenkova AY, Mosenko SV, Oleg S GlotovOS, Chernov AN, et al. Basic Predictive Risk Factors for Cytokine Storms in COVID-19 Patients. Front Immunol. 2021 Nov 10;12:745515.  doi: 10.3389/fimmu.2021.745515. eCollection 2021.
4. Bordoni V, Sacchi A, Cimini E,  Notari S,  Grassi G, et al. An Inflammatory Profile Correlates with Decreased Frequency of Cytotoxic Cells in Coronavirus Disease 2019. Clin Infect Dis. 2020 Nov 19;71(16):2272-2275.  doi: 10.1093/cid/ciaa577
5. Del Valle DM, Kim-Schulze S, Huang HH, et al. An Inflammatory Cytokine Signature Predicts COVID-19 Severity and Survival. Nat Med 26, 1636–1643 (2020). doi: 10.1038/s41591-020-1051-9
6. Song Y, Ye Y, Su SH, Stephens A, Cai T, et al. Digital Protein Microarray for COVID-19 Cytokine Storm Monitoring. Lab Chip. 2021 Jan 21; 21(2): 331–343. Published online 2020 Nov 19. doi: 10.39/d0lc00678e.

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CRISPR/Cas9 genome editing is fast becoming the essential tool for industrial microbial production and chemical synthesis by virtue of its speed, precision, and versatility. CRISPR can drastically improve the efficiency of industrial biotechnology through enzyme engineering and pathway modification and is already being put to work to change chemical manufacturing. Here we run through some of the most exciting applications of CRISPR.

Challenges in industrial biotechnology

Microorganisms have long been used in the synthesis of products that are useful for human societies. Yeast, for example, have been used in the production of beer and wine for millennia, with the underlying process of fermentation uncovered in the 18^th century by Louis Pasteur, who was the first to show how yeast convert sugar into alcohol.¹ Following the discovery of penicillin, industrial microbiology took off as the antibiotic was mass-produced to support the war effort.²

Today, microbes are being used to synthesize diverse products, from foods and drugs to cosmetics and biofuels. By 2026, the global synthetic biology market, covering tools, technologies, and applications, is expected to grow to more than $30 billion, with medical and industrial bioproduction leading the way.

Yet, challenges remain in translating advances in bioengineering into efficient mass production of products. Engineering new microbial strains for production is often time-consuming and expensive, requiring extensive optimization. This is further hindered by the speed at which microorganisms grow and the time it takes for complex biosynthetic reactions to complete, which can slow production and limit yields.

Other complicating factors include navigating the complexity of cellular metabolism, the requirement for large amounts of substrate, which may include precious natural resources, and excess byproduct production.³

CRISPR: a game-changing tool

The entry of CRISPR/Cas9 gene editing technology onto the commercial market has made it an indispensable tool in industrial microbiology. Discovered in bacteria by Nobel Prize winners Emmanuelle Charpentier and Jennifer Doudna, CRISPR/Cas9 makes precise cuts in DNA, using natural cellular repair mechanisms to inactivate a gene, delete a section of DNA, or even insert new genetic information.

On average, a bacterial genome contains around 30 biosynthetic gene clusters, potentially encoding 30 different natural products.⁴ CRISPR/Cas9 gene engineering is expanding the range of these products that are accessible to humans, by increasing the number of microbial strains that can be effectively used in industrial production and making more of their metabolic pathways suitably efficient for large-scale production.⁵ The possibilities of this technology for industrial biotechnology are almost limitless, as it combines precision with the versatility to manipulate complicated cellular processes in cells in a range of different ways.

CRISPR also enables multiplexing, so that multiple genes can be modified at the same time. It is easy to use, thanks to accessible licensing and a surge of third-party companies offering “ready to go” CRISPR-based genome engineering platforms.

Top CRISPR/Cas9-modified biosynthetic pathways

CRISPR/Cas9 gene editing is a powerful way of improving biosynthesis for organic and inorganic chemicals, and novel products that are not naturally produced by the host cell. Some of the top compounds being made using CRISPR-modified cells include carotenoids, citric acid, 1,3-propanediol, phenylethanol, and squalene.

Carotenoids are yellow, orange, and red pigments found naturally in a range of foods and plants. They are important nutrients and are also used to produce food colorings and fragrances for a variety of products. CRISPR/Cas mutation libraries have been used to identify more efficient enzyme variants in the production of carotenoids, resulting in an 11-fold improvement in carotenoid production in yeast.⁶

Citric acid is naturally found in citrus fruits but billions of tons are manufactured each year for use as an acidifier and flavoring agent. CRISPR/Cas9 has been used to increase the efficiency of its production in its workhorse organism, Aspergillus niger.⁷

CRISPR has also enhanced the production of 1,3-propanediol, an important building block in polymer production, used in a range of industrial products and as a solvent. Using CRISPR, the production of this compound has been increased by almost 50% in the bacterium Klebsiella pneumoniae.⁸

Phenylethanol is a fragrant alcohol used in food and cosmetics. CRISPR has been used to engineer the overexpression of this floral-scented compound by a stress-tolerant strain of yeast. The same strategy could be used to produce other aromatic compounds in this host.⁷

Squalene, a compound found in high quantities in shark liver oil, is an ingredient in cosmetics and used as an adjuvant in vaccines. CRISPR has been used to modify bacteria to produce squalene from glucose at high efficiencies, supporting more sustainable production.⁹

Enzyme engineering using CRISPR

One of the major applications of CRISPR in industrial biotechnology is the engineering of enzymes. CRISPR-guided DNA polymerases can be used to target nucleotides for mutagenesis, offering an extremely high targeted mutation rate and introducing a range of novel mutations that may be beneficial to enzyme function.

This approach has been used in E. coli, where the engineering of a new enzyme involved in amino acid synthesis increased the production of tryptophan—widely used in the food, feed, and medical industries by almost 40%.^4,14

CRISPR can also be used to optimize metabolic pathways by deleting, knocking down, or overexpressing genes. In a groundbreaking example, CRISPR/Cas9 was used to delete eight genes in a fatty acid production pathway in yeast, resulting in a 30-fold increase in free fatty acid production in a matter of days.¹⁵

Other approaches include knocking down a competing pathway using transcriptional repression (CRISPRi), which has been used to enhance the production of various compounds. The advantage of this approach is that the strength of repression can be modified depending on the combinations of genes targeted.

Similarly, transcriptional activation (CRISPRa) can be used to upregulate genes to increase production. Both CRISPRi and CRISPRa methods are highly useful for multiplex engineering, whereby several pathways in cellular metabolism can be finetuned at the same time.

The versatility of the technology is unmatched and many companies are starting to take advantage of this. For example, U.S.-based Gingko Bioworks has applied CRISPR/Cas9 to modify cells for a wide range of purposes, partnering with pharmaceutical companies, food producers, and cosmetics companies to bring the biosynthetic potential of this powerful technology to life.

Greener production

CRISPR supports more sustainable chemical production, which is a major concern for the industry at present. Through sophisticated pathway modifications, CRISPR is able to change the feedstocks that microbes are able to use. This is supporting a shift towards renewable or waste substrates and those that generate a higher yield of product.

CRISPR can also be used to modify microbes to grow at lower temperatures and, as in the case of squalene similar natural products, to generate a more sustainable source of compounds that reduces pressure on threatened plant and animal species. Biotechnology will be key to meeting the ever-growing demand for large-scale, sustainable chemical manufacturing. As current processes are optimized and new compounds come to market, CRISPR/Cas9 has a vital part to play in bringing forward this bio-industrial revolution.

References

1. Alba-Lois, L. & Segal-Kischinevzky, C. (2010) Beer & Wine Makers. Nature Education 3(9):17.
2. Buchholz, K. and Collins, J. (2013) Industrial Biotechnology. Sustainable Growth and Economic Success. Edited by Wim Soetaert and Erick J. Vandamme.
3. Chen, G.-Q. (2012). New challenges and opportunities for industrial biotechnology. Microbial Cell Factories, 11(1), p.111. doi:10.1186/1475-2859-11-111.
4. Chen, M., Chen, L. and Zeng, A.-P. (2019). CRISPR/Cas9-facilitated engineering with growth-coupled and sensor-guided in vivo screening of enzyme variants for a more efficient chorismate pathway in E. coli. Metabolic Engineering Communications, 9, p.e00094. doi:10.1016/j.mec.2019.e00094.
5. ‌Hille, F., Richter, H., Wong, S.P., Bratovič, M., Ressel, S. and Charpentier, E. (2018). The Biology of CRISPR-Cas: Backward and Forward. Cell, 172(6), pp.1239–1259. doi:10.1016/j.cell.2017.11.032.
6. Jakočiūnas, T., Pedersen, L.E., Lis, A.V., Jensen, M.K. and Keasling, J.D. (2018). CasPER, a method for directed evolution in genomic contexts using mutagenesis and CRISPR/Cas9. Metabolic Engineering, 48, pp.288–296. doi:10.1016/j.ymben.2018.07.001.
7. Li, M., Lang, X., Moran Cabrera, M., De Keyser, S., Sun, X., Da Silva, N. and Wheeldon, I. (2021). CRISPR-mediated multigene integration enables Shikimate pathway refactoring for enhanced 2-phenylethanol biosynthesis in Kluyveromyces marxianus. Biotechnology for Biofuels, 14(1). doi:10.1186/s13068-020-01852-3.
8. Lian, J., HamediRad, M., Hu, S. and Zhao, H. (2017). Combinatorial metabolic engineering using an orthogonal tri-functional CRISPR system. Nature Communications, 8(1). doi:10.1038/s41467-017-01695-x.
9. Park, J., Yu, B.J., Choi, J. and Woo, H.M. (2019). Heterologous Production of Squalene from Glucose in Engineered Corynebacterium glutamicum Using Multiplex CRISPR Interference and High-Throughput Fermentation. Journal of Agricultural and Food Chemistry, 67(1), pp.308–319. doi:10.1021/acs.jafc.8b05818.

10. Shi, S., Qi, N. and Nielsen, J. (2022). Microbial production of chemicals driven by CRISPR-Cas systems. Current Opinion in Biotechnology, 73, pp.34–42. doi:10.1016/j.copbio.2021.07.002.
11. Steele, A.D., Teijaro, C.N., Yang, D. and Shen, B. (2019). Leveraging a large microbial strain collection for natural product discovery. Journal of Biological Chemistry, 294(45), pp.16567–16576. doi:10.1074/jbc.rev119.006514.
12. Tong, Z., Zheng, X., Tong, Y., Shi, Y.-C. and Sun, J. (2019). Systems metabolic engineering for citric acid production by Aspergillus niger in the post-genomic era. Microbial Cell Factories, 18(1). doi:10.1186/s12934-019-1064-6.
13. Wang, X., Zhang, L., Liang, S., Yin, Y., Wang, P., Li, Y., Chin, W.S., Xu, J. and Wen, J. (2022). Enhancing the capability of Klebsiella pneumoniae to produce 1, 3-propanediol by overexpression and regulation through CRISPR. Microbial Biotechnology, 15(7), pp.2112–2125. doi:10.1111/1751-7915.14033.
14. Wen, X., Ning, L. and Jia, Z. (2017). Production of L-tryptophan by Microbial Fermentation. Progress in Applied Microbiology.
15. ‌Zhang, Y., Wang, J., Wang, Z., Zhang, Y., Shi, S., Nielsen, J. and Liu, Z. (2019). A gRNA-tRNA array for CRISPR-Cas9 based rapid multiplexed genome editing in Saccharomyces cerevisiae. Nature Communications, 10(1). doi:10.1038/s41467-019-09005-3.

Eric Rhodes is CEO at ERS Genomics, which provides worldwide licensing access to the essential CRISPR/Cas9 patent portfolio for commercial use.

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Exciting advances in establishing metrics to quantify underlying structure in vascular red blood cell distributions¹ will enable us to track individual cancer cells as they travel through crowded blood vessels, randomly careening with red blood cells on their journey through a patient’s body.

Why quantify cellular motion in blood flow

Until recently, producing an accurate representation of complex cellular motion and interactions throughout the elaborate anatomy of the circulatory system was beyond our reach. In the last few decades, we have seen an astounding increase in computational power and the ability to leverage large-scale supercomputers to capture realistic, 3D simulations of cellular models in the vasculature.^2,3 Now that we have the computational capability to successfully simulate the interactions of hundreds of millions of cells, the question turns to how we can use these realistic models to provide meaningful results.

Building on these advances, our team led by graduate student Sayan Roychowdhury, is working to use computational models to provide much-needed insight into the mechanics driving cancer cell transport. Predicting the path of a tumor cell allows us to know where it will likely arrive at a blood vessel wall and latch on to establish a secondary tumor site. Our bodies contain an intricate system of 60,000 miles of arteries, veins, and capillaries—a delicate network of vessels filled with over 25 trillion red blood cells. The specific path of a single tumor cell moving through this complicated and crowded superhighway depends on its random interaction with each group of red blood cells it meets along the way.

For example, imagine yourself trying to make your way through a crowded shopping mall during the peak of the holiday shopping season. You are constantly changing course to avoid bumping into people and altering direction when you accidentally step into someone else’s path. The way you progress through the mall’s packed corridors or a store’s congested aisles varies depending on how many people are around you, who they are, and where they are standing. Similarly, the cancer cell’s trajectory through any region of the circulatory system will be influenced by the location of cells around it, the type of those cells, and their individual orientations. The positions of the surrounding cells are never static. At any given point in time, the same location in space can be occupied by a completely different set of cells.

To understand the general motion of the cancer cell, a single simulation is inadequate for capturing the variation in cancer cell trajectory. On the other hand, simulating a cancer cell a million times with slightly perturbed neighboring cell positions is clearly untenable. Thus, a means of intelligent sampling is needed to determine how many different representations and orientations need to be simulated to adequately sample the space and provide meaningful results.

We needed many cell configurations to determine all possible trajectories of a cancer cell, but we didn’t know how many would be enough. So, we set out to determine a method of identifying randomly placed cell distributions and quantifying distinct cell configurations. But first, we had to develop specific ways to measure and describe all the different patterns representing the ways red blood cells are positioned in a group.

Supercomputer simulations

First, we generated a computer simulation of a large system packed with red blood cells before introducing a three-dimensional blood vessel into the simulation. The vessel was placed in the system and all the red blood cells that fit inside were saved in terms of location and orientation in space, to create a particular configuration. We generated several of these red blood cell configurations by refilling the vessel at different locations through the system.

Then, we applied the radial distribution function—the formula researchers commonly use to calculate randomness within a dense distribution in molecular dynamics models. This function confirmed the realistic, random placement and spacing of red blood cells in our simulation.

The next part of our investigation was focused on coming up with a method to numerically compare all the generated configurations. We applied the Jaccard index, a quantitative metric used to measure the similarity between sample sets, to determine if the groupings of red blood cells were distinct by comparing the amount of volume that each configuration shared in space. This required us to map the biconcave red blood cell shapes onto 3D grid points and calculate shared points for all the patterns. In the end, we were left with a calculable value to describe their similarities.

We found that, beyond a certain point, increasing the number of different red cell groupings does not significantly change the probability distribution. In fact, the distribution grew more predictable with higher numbers. As we watched the red blood cells randomly crowd through a cylinder narrower than a single strand of hair and shorter than a computer monitor’s pixel, we observed 72 unique configurations and concluded it was a sufficient size to predict parameters accurately for this vessel.

Due to the random pattern of red blood cell distribution, we needed a way to sample the full array of distributions if we hope to ascertain individual cell behavior. We established numerical methods to quantitatively measure and describe an array of cell distributions. This brings us one step closer to determining the set of cell distributions that best describes the entire assortment of possibilities.

Future directions

We hope, intelligently sampling cell configurations will allow us to utilize expensive supercomputer simulations more efficiently. This research is possible thanks to a code we developed, HARVEY,⁴ in 2013 to measure blood flow dynamics. We have optimized it to run efficiently on some of the world’s largest supercomputers.⁵ Over time, the use of high-performance computing has become more useful in biomedical research.

Simulations have given us new insights into disease progression and drug response. Further advances in biomedicine await our enhanced understanding of high-performance computers. Today, the fields of biomedicine and high-performance computing are inextricably intertwined. We can now simulate the movement and behavior of hundreds of millions of red blood cells simultaneously. With this new ability, we must answer the question of how we can use this information to draw real-world conclusions. The behavior of cells inside a blood vessel is just one application of this technology. We can simulate particles, atoms, and cells in a variety of situations.

References

1. Roychowdhury, Sayan, Erik W. Draeger, and Amanda Randles. “Establishing Metrics to Quantify Underlying Structure in Vascular Red Blood Cell Distributions.” International Conference on Computational Science. Springer, Cham, 2022.
2. Rossinelli, Diego, et al. “The in-silico lab-on-a-chip: petascale and high-throughput simulations of microfluidics at cell resolution.” Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2015. https://doi.org/10.1145/2807591.2807677
3. Ames, Jeff, et al. “Multi-GPU immersed boundary method hemodynamics simulations.” Journal of computational science 44 (2020): 101153. https://doi.org/10.1016/j.jocs.2020.101153
4. Randles, Amanda Peters, et al. “Performance analysis of the lattice Boltzmann model beyond Navier-Stokes.” 2013 IEEE 27th International Symposium on Parallel and Distributed Processing. IEEE, 2013. https://doi.org/10.1109/IPDPS.2013.109
5. Randles, Amanda, Erik W. Draeger, and Peter E. Bailey. “Massively parallel simulations of hemodynamics in the primary large arteries of the human vasculature.” Journal of computational science 9 (2015): 70-75. https://doi.org/10.1016/j.jocs.2015.04.003

Amanda Randles, PhD, is the Alfred Winborne Mordecai and Victoria Stover Mordecai assistant professor of biomedical sciences at Duke University and an NAI (National Academy of Inventors) fellow.

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According to Breden, genes of the adaptive immune system are some of the most complicated, duplicated, and highly evolved genes in vertebrates. “Trying to understand the immunoglobulin and T-cell receptor loci and the expression of the T-cell and B-cell repertoires require data standardization to facilitate sharing. But the process of implementing standardization is slow, takes community initiative, face-to-face work, and follow-up.”

This need led Jamie Scott, PhD, Tom Kepler, PhD, and Breden to brainstorm an Open Science grassroots community in 2014. Today, the AIRR Community is an official committee of The Antibody Society and develops and promotes standards and recommendations for obtaining, analyzing, curating, and comparing/sharing AIRR-seq datasets.¹ They validate tools to analyze AIRR-seq data and relate AIRR-seq datasets to other “big data” sets, such as microarray, flow cytometric, MiSeq, and single-cell gene-expression data, and address the legal and ethical issues involving the use and sharing of datasets derived from human sources.

The AIRR Community developed the AIRR Data Commons, which follow a distributed data model and is currently composed of seven globally distributed repositories that provide public access to more than 80 MiARR-compliant studies, including many COVID-19 studies, and the accompanying AIRR-seq data.² MiARR is a set of standards and protocols for curating and sharing the immense repositories.

The focus of the iReceptor platform is to federate the large AIRR Data Commons and to facilitate the curation, analysis, and sharing of these antibody/B-cell and T-cell receptor repertoires. The platform connects the distributed network, allowing queries across multiple projects, labs, and institutions. Over five billion sequences and 8,987 repertoires are currently available from seven remote repositories, 70 research labs, and 85 studies.

Present functionalities allow searching for repertoires satisfying certain metadata, repertoires that contain specific CDR3 sequences, and identified repertoires for sequences derived from particular V, D, and J genes and alleles. Sequences can be downloaded in AIRR.tsv format, easily importable to other AIRR-seq analysis tools, or analyzed through the Gateway with common tools.

Important new functionalities of the AIRR Data Commons and the iReceptor Gateway are the ability to curate, share, and analyze single-cell profiling data. This approach allows linkage of an immune receptor with the physiological state of the cell. “It is a huge advantage in trying to understand and predict the behavior of the adaptive immune system,” said Breden.

“Since single-cell sample sizes are smaller, sharing becomes even more important. The AIRR Data Commons are AIRR-seq data repositories from multiple laboratories that anyone can use,” said Breden. Located at Simon Fraser University, the iReceptor Gateway, an implementation of the vision of the AIRR Community, follows the AIRR Community standards and is part of the EU/CIHR funded iReceptor Plus Consortium.³

Single-cell immune profiling is only going to get more complicated and the data more difficult to curate. “Community-adopted standards are increasingly important,” said Breden. “The AIRR Community is open and transparent. We publish papers with standards we have developed and then distribute them to be voted on by the entire community to get buy-in. Anyone can actively become involved in a Working Group.”

To query or contribute to the AIRR Data Commons contact support@ iReceptor.org.

1. Trück J, Eugster A, Barennes P, Tipton CM, Luning Prak ET, et al. Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling (2021) eLife;10:e66274.
2. Christley S, Aguiar A, Blanck G, Breden F, Bukhari SAC, et al. The ADC API: A web API for the programmatic query of the AIRR Data Commons. (2020) Big Data; 3:22.
3. Corrie BD, Marthandan N, Zimonja B, Jaglale J, Zhou Z, et al. iReceptor: a platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories. (2018) Immunol Rev.; 284(1): 24–41.

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New research methods are being continuously developed and refined to better understand complex molecular events in infection cycles, determinants of pathogenesis, and host immune responses, with the aim of exploiting vulnerabilities in pathogens and identifying druggable targets in hosts to treat clinical manifestations of infectious diseases.

“Spatial transcriptomics, high-content analysis, organoids and organ-on-chip platforms, functional genomic screening, and AlphaLISA protein-protein interaction assays are the top methods that will advance our understanding of infectious diseases research and drug discovery,” says Karin Boettcher, PhD, strategy leader of life science research at PerkinElmer, who did her doctoral and postdoctoral work in microbiology and parasitology at the University of Goettingen, Germany.

Spatial insights into infection

Recent innovations in spatial transcriptomics have provided new insights into infectious disease biology by enabling the analysis of gene expression at the single-cell level while maintaining morphological context of the tissue. This not only allows exhaustive cataloging of cells present at sites of infection in the host, but also analyses of signal transduction mechanisms that permit or promote pathogen survival in microenvironmental niches in the host.

“Elucidating host-pathogen interaction at a cellular and molecular level to facilitate patient stratification and clinical decision-making requires next-generation methodologies, especially given the rapid introduction of new therapies,” says Boettcher.  “Automated confocal imaging combined with quantitative image analysis is very important in making the analysis of large tissue sections feasible. However, spatial transcriptomics requires human or mouse tissue samples which might not always be available.”

Relevant models

A prerequisite to studying pathogens, including viruses such as SARS-CoV-2 and their variants, is the ability to culture such pathogens in vitro. A conventional approach to developing cell culture models for new pathogens has been to genetically engineer cell lines to express the host membrane receptor that the virus uses to enter the host. “However, these cell lines are not physiologically relevant and not informative about virus or pathogen biology in vivo,” Boettcher cautions.

For example, the human lung-derived cell line MRC5, which is highly susceptible to infection by human coronaviruses 229E and OC43, is resistant to SARS-CoV-2. Therefore, scientists have stably transfected MRC5¹ cells with a lentiviral vector encoding angiotensin-converting enzyme 2 (ACE2) to induce viral infection and replication in these cells and used these mutagenized cells as models for the development of antiviral and vaccines.

“Advanced cell culture models such as iPSC (induced pluripotent stem cell) derived organoids or organ-on-chip platforms encompassing several cell types of the target organ as well as immune cells are critical for disease modeling and identification of new treatment options,” says Boettcher.

The use of physiologically relevant cell culture models has been limited in infectious disease research until now, but COVID-19 has initiated new investigations in this direction. SARS-CoV-2 infections have been explored in airway-on-chip models, as well as human bronchial, kidney, intestinal, liver ductal, and brain organoids.

High-content analysis

Physiologically relevant, complex cell culture models are just the first step. To interrogate these models, one needs effective imaging and analytical tools such as high-content screening and analysis (HCS/HCA). This combines high-speed automated imaging at single-cell resolution with parallel pixel analysis to extract actionable data from infected cellular or organoid models.

“While keeping the cell models intact, HCS allows multiplexed readouts, such as the extent of infection, target cell types, morphologies of host cells, and much more,” says Boettcher. “Infectious disease researchers having used qPCR in the past to quantify infections, love the fact that all this information can be collected in a single well in one experiment.”

HCA has enabled studies on HIV-associated neurocognitive disorders (HAND) to decipher the effects on various therapeutic agents such as nucleoside/nucleotide reverse transcriptase inhibitors on the viability, structure, and function of glutamatergic neurons and primary human neural precursor cells.²

Host-pathogen interactions

Some pathogens, such as viruses, need the cooperation of factors in the host cell to establish infection. Targeting allied factors in the host constitutes a viable strategy to inhibit such pathogens. A key advantage to targeting the host rather than pathogen proteins to contain infection, is that compared to virus or parasite proteins, host proteins are less prone to mutations. Host-directed therapeutics, therefore, are less prone to drug resistance. Moreover, targeting host factors that act as gatekeepers could potentially inhibit entire families of pathogens rather than a single culprit.

“Elucidating essential host cell proteins and protein interactions between pathogen and host cell proteins is key to finding innovative drug targets,” says Boettcher.

CRISPR or siRNA-based functional genomic screens on whole genomes have been used to understand host-pathogen interactions for several key pathogens, including Zika virus, Dengue virus, and SARS-CoV-2.³ These approaches continue to be key in infectious disease research and drug discovery.

Once a molecular interaction has been detected between a viral component and a virus interacting protein (VIP)—a host cell protein that physically binds a viral protein, RNA, and/or DNA—it needs to be validated using multiple parallel assays that can be scaled up for high-throughput drug screening.

“This can be achieved with AlphaLISA protein-protein interaction (PPI) assays that enable PPI inhibition screening at scale,” says Boettcher. “AlphaLISA assays have been used to confirm SARS-CoV-2 and Zika VIPs.”

Therapeutic strategies

Infectious diseases are a global public health concern, and the convenience of air travel creates ample opportunity for the rapid spread of infections across physical and political borders. Yet certain infectious disease shows distinct regional preferences. For example, malaria is caused by the protozoan Plasmodium falciparum which is endemic to tropical countries.

Although therapeutic strategies against infectious diseases target either the pathogen or the host, these approaches are modulated by management of clinical manifestations and complexities of host-pathogen interaction, particularly the effect of sup-optimal host immune responses on the evolution of pathogen species that “learn” to express surface proteins to evade future host immune attacks.

“Traditionally, antibiotics are used to treat bacterial infections, antimicrobials against fungi or parasites, and antiviral agents to combat viruses. Therapeutic compounds or biologics either treat or diminish the symptoms of specific infectious diseases. Preventive measures such as vaccines have been successful in either preventing infection or minimizing the symptoms of the disease,” says Anis Khimani, PhD, senior strategy leader of life sciences at PerkinElmer, and who has conducted research in virology, pathogenesis, and vaccines at Harvard Medical School, Dana-Farber Cancer Institute, and at the Boots Pharmaceutical Company in U.K., and India.

The past decade has witnessed the rapid development of immunotherapies, including monoclonal antibodies, T-cell therapies, checkpoint inhibitors, and RNA-based therapies. Checkpoint inhibition, previously solely used in cancer treatment, is now a viable treatment option for infectious diseases such hepatitis B virus (HBV) infections.⁴

Innovations to combat drug resistance

The emergence of resistance of pathogens against existing therapeutics is a key impetus for continued innovation in drug discovery and repurposing approved or pipeline candidates for other indications.

“Resistance to drugs is noted across a number of infectious disease pathogens such as tuberculosis, hospital-acquired infections, malaria, and HIV,” says Khimani. “For example, emerging strains of HIV have been identified that are resistant to the multiple drug anti-retroviral therapy (ART).” Strains of HIV resistant to ART, therefore, require new drugs.

Reverse vaccinology, driven by artificial intelligence and structure-guided immunogen design, has been recently used to design vaccines against SARS-CoV-2 and other pathogens. It is a bioinformatic approach that screens all genes in a pathogen to identify those that confer antigenicity—the capacity of a chemical structure, usually on the surface of a pathogen to trigger an immune response in the host, and other desirable features for vaccine targets. Reverse vaccinology helps identify new vaccine candidates that are then synthesized and screened in vivo.

“Leveraging AI can also expedite clinical research and trials streamlining the process and subsequent monitoring,” says Khimani.

Vaccines, monoclonal antibodies, and checkpoint inhibitors continue to be explored against viral, bacterial, and parasite infections. In addition to the use of attenuated whole pathogens, vaccines now include mRNAs and plasmid-DNAs that have been used against a range of parasitic infections such as malaria, leishmaniasis, and toxoplasmosis.

“Nucleic acid vaccines can stimulate innate and adaptive immune responses and can overcome limitations encountered by peptide vaccines,” says Khimani.

Monoclonal antibodies, on the other hand, are designed to directly neutralize the pathogen, or to ease inflammation that follows infection. Khimani says, “For example, the anti-IL6 mAb, Tocilizumab, has shown to demonstrate efficacy in cases with cytokine storm following infection with SARS-CoV-2. Also, monoclonal antibodies against P. aeruginosa and S. aureus are in development, with potential for a broad-spectrum effectivity across isolates.”

Although still early in their development, antibody-antibiotic conjugates (AACs) are an emerging class of biotherapeutics for infectious diseases that ensure targeted delivery of antibiotics against bacterial infections. “Strategies combining novel immunotherapy targeting with conventional therapy show promising outcomes with more resilient pathogens, as well as potentially a broader coverage across variants within the same family of infectious agents,” adds Khimani.

The growing specter of drug resistance compounded by the emergence of new pathogens will require cross-disciplinary technical strategies not only in basic infectious diseases research but in developing effective therapeutics that give our species a winning chance in future host-pathogen feuds.

References

1. Uemura K, Sasaki M, Sanaki T, et al. MRC5 cells engineered to express ACE2 serve as a model system for the discovery of antivirals targeting SARS-CoV-2. Sci Rep. 2021 Mar 8;11(1):5376.
2. Smith AS, Ankam S, Farhy C, et al. High-content analysis and Kinetic Image Cytometry identify toxicity and epigenetic effects of HIV antiretrovirals on human iPSC-neurons and primary neural precursor cells. J Pharmacol Toxicol Methods. 2022 Mar-Apr;114:107157.
3. Gootenberg JS, Abudayyeh OO, Lee JW, et al. Nucleic acid detection with CRISPR-Cas13a/C2c2. Science. 2017 Apr 28;356(6336):438-442.
4. Chen Y, Tian Z. HBV-Induced Immune Imbalance in the Development of HCC. Front Immunol. 2019 Aug 27;10:2048.
5. Stagg NJ, Katavolos P, Achilles Poon K, et al. Nonclinical toxicology development of a novel antibody antibiotic conjugate for treating invasive Staphylococcus Aureus infections. Toxicol Appl Pharmacol. 2022 Jan 15;435:115811.

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“At the individual level, responders are 100% responsive and non-responders are 0% responsive. What patients are looking for is to know, to the best of their knowledge, where they fit into the response expectations,” says Nagourney.

The technique, called EVA/PCD (Ex Vivo Analysis of Programmed Cell Death) and developed by Nagourney’s team, assesses which drugs cause cancer cells to die and is gaining popularity in the treatment of a variety of cancers, particularly advanced-stage pancreatic cancers.

In addition, the team has compiled a large database of over 10,000 human cancer studies that use the EVA/PCD protocol in breast, ovarian, lung, pancreatic, and other types of cancers. Using the database in clinical decision-making, they report a two-fold increase in clinical response and improved one-year survival in over 2,500 published patient outcomes.

The database allows categorizing average patients into those above and below average with performance characteristics of about 80% sensitivity and 80% specificity. “Using this approach, our objective response rates improve by a factor of 2.04, p<0.001, and our one-year survival is higher by 1.44-fold, p=0.02,” says Nagourney.

Key methods in cancer research

According to Nagourney, phosphoproteomics, metabolomics, and primary cultures of human tumors are the three top methodological advances in cancer research, diagnostics, and therapeutics. Commenting on the importance of focusing on protocols and detailed methodologies, Nagourney says, “We have spent an enormous amount of time on developing resources, diagnostics, and drugs, but I think we have given short shrift to technologies that connect these developments to clinical therapy. Methodology that turns gee-whiz science into practical utility has been lacking.”

Genetic abnormalities that lead to cancer result in molecular anomalies that could serve as diagnostic, prognostic, and predictive biomarkers for the disease. Identifying these biomarkers in different tumorigenic pathways makes it possible for clinicians to select the most appropriate therapy for each patient. “When talking of [method-based] breakthroughs—phosphoproteomics, epigenomics (Peter Jones’ or Steven Baylin’s work), or other developments critical in advancing [cancer] therapeutics—my bent is to say those technologies that more closely approximate phenotypes are closest to the biological behavior of a cell.”

Phosphoproteomics is a promising method that helps identify biomarkers to diagnose disease progression and assess therapeutic efficacy, and new targets that can be drugged to treat cancer. Functional, physical, and chemical interactions among proteins are often orchestrated in time and space through post-translational phosphorylation. Therefore, understanding the interconnected gamut of phosphorylated proteins provides key signatures of cancer that can be used to develop a more granular understanding of specific disease phenotypes and determine optimal and personalized treatment paradigms.

“I am a great believer in metabolomics. Our laboratory includes mass spectrometry capabilities because we believe [the metabolome] is a very good approximation of phenotype,” says Nagourney. “Human tumor primary cultures offer the ultimate phenotypic expression in real time.”

Penetrating problems

Nagourney began his career studying hematologic malignancies. His early papers focused on the ability to induce programmed cell death in primary suspension cultures and predict the outcomes of childhood leukemia.

“I did the first work on a drug called 2-chlorodeoxyadenosine. We discovered it was curative for hairy-cell leukemia. The beauty of it was that you had a primary culture—a human tissue that gave you an immediate handle on how a cell responds to stress and the appropriate drug or drug-combination inducing stress that led to cell death,” says Nagourney.

However, when Nagourney moved from Scripps to UC Irvine and attempted to apply the same method to predicting outcomes in solid tumors, he ran into obstacles.

“When you make the jump from leukemias, which are single-cell suspensions, to solid tumors, all that lovely data is pulled apart. Disaggregated cellular systems were not predictive of solid tumor biology. We had to go back and explore what constituted a good solid tumor model. We changed our disaggregation, enzymatic, and density gradient technologies, and began to turn our pure little cultures into dirty cultures—clusters and aggregates. When we began to study solid tumors in aggregates, the predictive validity began to approach that of leukemia data.”

Over the years, Nagourney and his team have moved from studying “clean” single cell suspensions to messier cultures that closely approximate the native state of cancer tissue and contain all elements of the in vivo tumor microenvironment, including stroma, intercellular messengers, inflammatory cells, vascular elements, and cytokines. “We began to work in micro-aggregates or spheroids. Now we might call them explants.”

Heterogenous primary tumor cultures, although more representative of reality, were accompanied by new challenges. “For example, many drugs do not permeate multiple levels of cells. You have to adjust the aggregate size so that you get diffusion throughout the tissue aggregate. If the cell clusters are too large, larger drugs like doxorubicin, cannot permeate. There is no vasculature, but you have aggregates that still have their vascular communications intact.”

In the absence of blood vessels infiltrating primary tumor explants cultured in vitro, ensuring proper diffusion is essential.

“Once we optimized the right size and dimensions of the tumor [explants] we achieved appropriate permeation of the drugs into the microenvironment and began to get nice dose response curves. That’s the technology we now use,” explains Nagourney.

Heterogeneity of cellular architecture in tumors in vivo suggests that the location of specific cancer cells in primary spheroid cultures and, in particular, the degree to which their location ex vivo mimics in vivo geographic orientations, determines how predictive the assay results might be. Nagourney says, “We seek to injure [cancer] cells and induce cell death under conditions that mimic or recreate the in vivo condition. It’s not a perfect reproduction. It would be interesting to examine, for example, how might the proximity to the blood vessels influence drug sensitivity. We have not gone down to that level of granularity.”

In his ex vivo assays to gauge the response of smaller drugs, Nagourney often finds cell death across the entire spheroidal explant population. Monoclonal antibody studies using spheroids are a different matter, however, since these are large molecules and do not permeate into the interior of explants.

“We developed a technique that we used to study cetuximab in its original evaluations before it was approved. We were examining cetuximab alone and in combination with small molecule TKIs (tyrosine kinase inhibitors) for EGFR (epidermal growth factor receptor) and made some interesting discoveries based on our onion peeling approach. We could see layers of cells that were exposed to the monoclonals peeling off as they died, leaving a central viable core. That’s about as far as we’ve extended the granularity of assessing cell distributions,” says Nagourney.

The platform is developed to test small molecules that permeate into explants. Nagourney’s team is currently working to apply the technology to test the efficacy of larger drugs and cell therapies.

“We are getting very close to having a methodology that uses this platform to examine checkpoint inhibitors,” says Nagourney. This method assesses cytolytic effects of activated T cells. The challenge in maintaining patient-derived tumor explant cultures in a state as close as possible to their native state is that they cannot be propagated, amplified, or sub-cultured.

“One of my concerns for groups who are doing propagated 3D spheroids is that they will not be as predictive,” says Nagourney. “What we have to convince people is that these are primary culture explants in their native state.” The team is currently using the approach to study the cytolytic activity of tumor-infiltrating lymphocytes (TILs).

Nagourney is hesitant to use commercially available 3D organoid culture systems to study cancer, although he recognizes the burgeoning interest in using such models in testing drug toxicity. “My problem with tumor organoids that are propagated from cellular materials that then grow into 3D cultures is that they are for the most part monocultures, which are not representative of the actual state of affairs in human tissue. We are concerned that as these technologies grow in the cancer field, they may not be predictive.”

Explant studies in tumor biology have gone through a lot of fits and starts, since their inception in the 1950s. “It went through a period when people were propagating cells in 2D, doing clonogenic assays and thymidine incorporation. That didn’t work and people ran away from the field in the 80s and early 90s.”

Two fundamental advances renewed success in the face of prior failures. The first was the recognition of programmed cell death as a therapeutic goal, where cancer cells aren’t just prevented from growing and dividing but are induced to die. The second is the development of 3D heterogenous culture systems.

Nagourney’s team does not limit its histological and metabolomic analyses to studying apoptotic markers of cell death. “Apoptosis is only one form of cell death—there is necroptosis, ferroptosis, and autophagic changes. These are not captured if you simply look at caspase activation,” explains Nagourney. “In hematologic tumors, the caspase3,7 activity was a good correlate. In solid tumors, it is not.”

Nagourney prefers global assessments of cell viability to techniques such as BH3 loading that forces cell death through apoptosis. “A cell dying in culture by apoptosis that in the body would not die by apoptosis will give false results,” says Nagourney. His team captures both apoptotic and non-apoptotic cell death by assaying mitochondrial function, ATP content, and histochemical staining to assess morphological attributes.

“Our original work was largely morphologic,” notes Nagourney. “The delayed loss of membrane integrity, visible under the microscope with histological stains and counterstains, remains the team’s most reliable measure for assaying cell death, particularly in the context of drug development.”

In addition, Nagourney’s team uses luminometer-based assessments of ATP content and MTT and XTT endpoints to assay mitochondrial succinate dehydrogenase activity as a metabolic measure of cell death.

Orthogonal validation

“We tend to do a number of different measures as we run these dose response curves so that we get different angles on what is causing cell death. Morphologically you can often distinguish an apoptotic cell, which has characteristic features, from a more autophagic cell and a necroptotic cell.”

Scoring how cells appear to be dying from morphologic features gives the team a good handle on how a drug affects a cancer cell and the molecular signaling pathway that induces it to die.

Cancer cells change constantly to metastasize, evade therapy, and adapt to immune pressure. The U.K.’s TRACERx (TRAcking Cancer Evolution through therapy) project led by Charles Swanton, FRCP, PhD, at the Francis Institute, is tracking how cancer cells vary as they migrate from primary to metastatic lesions. The multi-million-pound initiative aims to improve diagnosis, better tailor therapies, and forestall recurrence. During the early stages of the disease, cancer cells express what Swanton described as “truncal neoantigens” that involve a select set of tumor suppressor genes such as p53 or KRAS.

“Over time, under conditions of stress tumor cells will develop new tricks that allow these stressed cells to survive new conditions. You might see variations of mutations that change the course of the therapy,” says Nagourney. In vitro assays reflect these changes in cancer progression observed in vivo and are equally predictive in testing metabolic and morphologic markers in primary and metastasized solid tumors.

“Most of our patients come to us at fairly advanced stages. Very often they have liver metastasis or extensive lymphadenopathy. We get a biopsy of that. At this stage, they do become somewhat more resistant. We often feel that since they share the truncal mutation and may have acquired new mutations, if we can find activity in those distant sites, it often characterizes the population as a whole—both primary and metastatic.”

Cancer cell death

Nagourney is a great believer in the work of John Reed. “His group suggests cancer is a disease of cell survival. Ultimately cancer is a cell that wants to stay alive,” says Nagourney. Although a cancer cell may proliferate before it is targeted by a drug, Nagourney is not interested in proliferation. He says, “The endpoint of cell death is the most important measure in a cancer laboratory because only a living cell can proliferate, and a dead cell cannot kill you.”

In a 2018 paper in Oncotarget and a 2021 paper in Gynecological Oncology, Nagourney’s team explored the metabolic features that enable cancer cells to stay alive and outsmart the immune system. “These can often be measured metabolically in the blood or in tissue culture media. The signatures correlate with drug resistance. There is a continuum from the state of a cell gaining a survival advantage, utilizing that survival advantage to remain alive to ultimately propagating and metastasizing. They use those same survival advantages to resist chemotherapy and other targeted therapies.”

These subtle abnormalities in cell biology that ultimately result in cancer are similar to inborn errors of metabolism, believes Nagourney. “Metabolic studies may for the first time give us a handle on the phenotype from a drop of blood. We’re extremely excited by the possibility of applying that in the clinical setting.”

Database

Nagourney’s team has compiled laboratory analyses of over 10,000 patients into a comprehensive proprietary database. The database enables the prediction of whether a patient is likely to respond to a specific treatment, with a high degree of accuracy. Through contracts with various nations, the institute offers the database as a service to patients around the world.

“For almost every type of cancer, if you ask me, is this degree of say, taxane or platinum likely to confer response? Based on the distributional analyses of the continuum of sensitivity and resistance, we can place every patient against their cohort,” points out Nagourney.

The database also enables in vitro Phase II trials. “It’s an excellent way for the pharmaceutical industry to have a bridge between, as I mentioned previously, gee-whiz science and practical utility,” he says.

Nagourney’s team has used the database to test the efficacy of a drug called gemcitabine for Eli Lily. Gemcitabine, a difluorodeoxycytidine originally developed as an antiviral, was repurposed for the treatment of sarcoma, ovarian, lung, lymphoma, and bladder cancers. “The laboratory results predicted virtually all of the ultimate FDA approvals.”

Toward precision

Despite robust predictive advantage, Nagourney laments that these technologies have been underestimated in clinical practice and decried in editorials.

“They continue to suggest these technologies need to prove they save lives. I have never seen a clinical trial where a genomic platform has been forced to show it saves lives,” he says. “What we prove is the performance characteristics of the test within the standards.”

Nagourney believes initial errors of reasoning that led to failed strategies of blocking cell proliferation, and disaggregating tumors into single cells instead of focusing on cell death, has created a bias against the technology. “Once you use this technology, it’s very hard to go back to clinical oncology as it is practiced.”

Nagourney believes guideline-driven medicine is increasingly on a collision course with precision medicine. “We better get on the right side of that,” says Nagourney, “because what we’re seeing is increasing standardization of therapies while patients are clamoring for individualized care.”

Nagourney celebrates the recent advances in genomics, transcriptomics, gene editing using CRISPR, epigenomics, and proteomics.

“But the ultimate measure of the human phenotype is cell biology studies,” he explains. That’s going to be so important to grasp the complexity of cell behavior. We need to be humble in our scientific pursuits so that we allow this extraordinary biological complexity, redundancy, and promiscuity of events to teach us. In my work, I get the answer—other people have to provide the question.”

Nagourney hopes the in vitro tumor explant testing platform in combination with the database could be the key to achieving personalized care so that cancer patients are treated based on the latest research optimized for their specific condition and not just what has been approved for a narrow, generalized indication.

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The technology

“FUS is analogous to using a magnifying glass to focus beams of light on a point to burn a hole in a leaf but, instead of an optical lens, an acoustic lens is used to focus multiple beams of ultrasound energy to target deep in the body with a high degree of precision and accuracy, sparing the adjacent normal tissue,” said Neal Kassell, MD, founder and chairman of the Focused Ultrasound Foundation. “Each of the individual beams only has the power of diagnostic ultrasound until the focal convergence point.”

The application of MR-guidance in thermal ablations through high-intensity focused ultrasound (HIFU) continues to be the subject of numerous studies and clinical trials.¹ Though potential clinical applications of HIFU were reported as early as the 1950s, precise targeting was a major issue. Finally, in 2004, the first MRI-guided HIFU application received FDA approval for the removal of uterine fibroids.³

Ultrasound enables deep tissue treatment, improves focus on the target tissue through its small wavelengths, and offers precise control over the shape and location of energy deposition. It is noninvasive, extracorporeal, and nonionizing as compared to conventional cancer treatment methods like chemotherapy, radiotherapy, and open surgery.⁴

At the other end of the acoustic spectrum, low-intensity FUS (LIFU) is frequently used for locally and reversibly eliciting excitatory and inhibitory neuromodulation or to facilitate drug and gene delivery through the permeabilization of the blood-brain barrier.¹

“Currently, we know 25 different ways that FUS affects tissue at the focal point. A decade ago, we understood three,” said Kassell. ”This creates the opportunity to treat a large variety of disorders as compared to radiation therapy or a surgical robot.

“FUS can be used to destroy tissue and to deliver therapeutic agents to the point in the body where they are needed, minimizing systematic side effects and thereby enhancing treatment effectiveness and safety. “The effect is immediate and verifiable.”

Therapeutic potential

Today, worldwide, treatments for nearly 170 clinical indications are in various stages of R&D and commercialization, up from three a decade ago, according to Kassell. “We just began getting coverage and reimbursement from government and commercial organizations,” he said.  “One of our challenges is to not have FUS as one of medicine’s best-kept secrets. FUS is the most powerful sound you will never hear but it is the sound that can one day save your life.”

FUS use continues to grow. The applications that excite Kassell most are for brain indications like Alzheimer’s, Parkinson’s, ALS, Huntington, OCD, depression and other neuropsychiatric disorders⁵, dystonia, epilepsy⁶, brain tumors, stroke, as well as for oncological indications and immunotherapy.

In a defined area, FUS can reversibly open the blood-brain barrier to allow access of therapeutic agents. Microbubbles, hollow lipid spheres approximately a tenth of the diameter of a red blood cell, can be packaged with therapeutics and millions injected intravenously. They burst and release the pharmacological payload at the FUS convergence point. The great promise of FUS combined with circulating microbubbles is reflected by a rapidly growing number of clinical trials to treat various brain diseases.^7,8

“FUS is disruptive to physicians’ practices, referral patterns, and manufacturers of displaceable legacy therapeutic equipment. The evolution of any highly disruptive technology from idea to widespread utilization as a global standard-of-care is a glacial process. Every month that goes by that this amazing technology is not available results in unnecessary death, disability, and suffering for countless people,” said Kassell.

References

1. Kamimura HAS, Conti A, Toschi N, Konofagou EE. Ultrasound neuromodulation: mechanisms and the potential of multimodal stimulation for neuronal function assessment. Front Phys. 2020; 8:150.
2. Focused Ultrasound Foundation, 2022 State of the Field.
3. Shehata Elhelf IA, Albahar H, Shah U, Oto A, Cressman E and Almekkawy M. High intensity focused ultrasound: The fundamentals, clinical applications and research trends. Diagnostic and Interventional Imaging 2018 99, 349—359.
4. Izadifar Z, Izadifar Z, Chapman D and Babyn P. An Introduction to High Intensity Focused Ultrasound: Systematic Review on Principles, Devices, and Clinical Applications. Clin. Med. 2020, 9, 460; doi:10.3390/jcm9020460
5. Wang JB, Di Ianni T, Vyas DB, Huang Z, Park S, Hosseini-Nassab N, Aryal M and Airan RD. Focused Ultrasound for Noninvasive, Focal Pharmacologic Neurointervention. Neurosci. 2020 14:675.
6. Lescrauwaet E, Vonck K, Sprengers M, Raedt R, Klooster D, Carrette E and Boon P. Recent Advances in the Use of Focused Ultrasound as a Treatment for Epilepsy. Neurosci. 2022 16:886584.
7. Chen S, Nazeri A, Baek H, Ye D, Yang Y, Yuan J, Rubin JB and Chen H. A review of bioeffects induced by focused ultrasound combined with microbubbles on the neurovascular unit. Journal of Cerebral Blood Flow & Metabolism 2022, Vol. 42(1) 3–26. DOI: 10.1177/0271678X211046129
8. Wu SK, Tsai CL, Huang Y and Hynynen Focused Ultrasound and Microbubbles-Mediated Drug Delivery to Brain Tumor. Pharmaceutics 2021, 13, 15

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Molecular predictors of response to obesity treatment are key in combating the obesity epidemic. Toward this end, the National Institutes of Health (NIH) launched ADOPT (Accumulating Data to Optimally Predict Obesity Treatment Core Measures) in 2018 which collects behavioral, biological, environmental, and psychosocial data to identify predictors of response to obesity treatment.¹

Variables for weight loss

A clinical trial² conducted at the Mayo Clinic by a team led by Andres Acosta, MD, and Michael Camilleri, MD, stratified obesity into four distinct observable traits or phenotypic categories based on underlying pathophysiology, and prescribed anti-obesity medications—phentermine, phentermine/topiramate, bupropion/naltrexone, lorcaserin, and liraglutide—according to the patient’s phenotype. The study conducted on 450 participants measured body composition, resting energy expenditure, satiety, eating behavior, and physical activity, among other parameters, using standard assays and questionnaires. Based on these parameters, the investigators were able to assign one of the four phenotypes to 383 of the 450 participants.

The phenotype-guided approach was linked to a 1.75-fold greater weight loss after a year, and the fraction of patients who lost more than 10% body weight at the end of the year was 79% compared to only 34% among patients who were prescribed medications without phenotyping.

Acosta and Camilleri went on to found Phenomix Sciences, a precision obesity biotechnology startup that aims to predict obesity phenotypes to improve outcomes of weight loss treatments in patients. With financial backing from the American Medical Association’s innovation arm, Health2047, and United Healthcare, Phenomix is developing a first-of-its-kind therapy selection blood test (Phenomix Sciences Obesity Platform) that predicts a patient’s response to GLP-1 (Glucagon-like peptide 1) treatments, such as Wegovy (semaglutide). The test is based on a proprietary biobanking registry that includes data collected from over 1,000 patients treated for obesity at the Mayo Clinic.

Mark Bagnall, CEO of Phenomix Sciences, said, “This is an exciting time in the evolution of obesity medicine. The justifiable excitement about new weight management products such as semaglutide and tirzepatide has highlighted the value of rigorous research in the field.”

Phenotyping finesse

The team led by Acosta and Camilleri measures how patients respond on treadmill tests, their body composition, and gut mobility, among other things. “You get fed a radio-labeled omelet and neuroimaging tells you how quickly the omelet passes through your GI tract,” said Bagnall.

Based on detailed patient data on genetics, metabolomics, hormones, and behavior, the therapy selection test phenotypes patients into four obesity categories. These include hungry brain (satiation), hungry gut (satiety), emotional hunger (reward), and slow burn (metabolism). The satiation phenotype is categorized by the excessive consumption of calories without feeling full, while the satiety phenotype is marked by feeling hungry shortly after eating. Eating in response to emotional triggers is the hallmark of the emotional hunger phenotype, and a slow metabolism where the body burns calories ineffectively, indicates the slow burn phenotype.

Whereas clinical trials can conduct comprehensive phenotyping studies on a significant sample, pre-pandemic data estimates 41.9% of adults in the United States are obese.³ The pandemic might have worsened this statistic. Such a large patient population creates obvious challenges in scaling the ability to predict treatment responses through individual studies.

“The problem is you can’t send 100 million people to the Mayo Clinic for eight hours of observational analysis,” said Bagnall. “Our job as a company is to translate those observations into a blood test. Our test analyzes about 2,000 SNPs based on which we determine the four primary obesity phenotypes.”

Refining predictions

Bagnall believes that the company’s partnership with the Mayo Clinic biobanking study will continue to generate more data that will help the company refine its algorithm and the predictive set of SNPs for a better understanding of obesity and a more accurate phenotyping of patients from diverse populations.

Data analysts know the predictive value of large datasets is only as good as the data fed into the database. Clinical data can be messy and certain data points may not be available for all patients, requiring computational corrections for missing data. Rigorous quality control measures are therefore key for a functional database that enables accurate predictions.

“The big picture is to work with individuals and institutions who know what they are doing, whom you can trust. Part of the reason we work with Mayo Clinic is their level of rigor when it comes to onboarding patients and documenting their background,” said Bagnall. “Then we have internal controls on our side to make sure that we can pick out anomalies in the data we are receiving. There are checklists at both ends. So, we are able to quickly ensure that data is properly dovetailing.”

Precision medicine approaches recognize that obesity may result from a range of pathophysiological mechanisms. Therefore, not all patients respond to the same treatment. By enabling access to clinical and molecular data linked to stages and types of obesity, the biobanking registry helps evaluate variability in obesity treatment responses using a data-driven approach that provides personalized treatment options for patients.

“Our biobanking agreement with Mayo Clinic is an important opportunity to make vast strides in how we understand the complexities of obesity treatment. We believe the biobanking registry investment will better support obesity centers by providing concrete evidence and insights into how DNA and other factors need to be considered in treatment,” said Bagnall.

Age, race, gender, education, socioeconomic status, and behavior compound genetic factors that underlie obesity. Accurate phenotyping can pinpoint a patient’s specific cause of weight gain and match the patient to the optimal treatment option for their phenotype.

“The upside is significant for patients and payers. Patients get the right treatment the first time and payers avoid paying for a costly trial-and-error approach,” said Bagnall. “There are still physicians who believe that obesity is more a moral failing than a true disease. That is something that we in the healthcare community need to fix.”

Obesity was designated a disease by the American Medical Association in 2013. This has begun to dissolve the misconceptions surrounding the chronic, multifactorial disease. In addition to establishing clear phenotypes, phenotype-genotype correlations, and molecular mechanisms, it is also important to recognize the role of genotypic and phenotypic diversity in the various manifestations of obesity.

Phenotype-driven precision medicine holds the potential to change the future of obesity treatment through better-informed clinical decisions, determining coverage, and effectively combining diagnostic testing with drug therapy.

References

1. Rosenbaum M, Agurs-Collins T, Bray MS, et al. Accumulating Data to Optimally Predict obesity Treatment (ADOPT): recommendations from the biological domain. Obesity (Silver Spring) 2018;26(suppl 2):S25-S34.
2. Acosta A, Camilleri M, Abu Dayyeh B, et al. Selection of Anti-obesity Medications Based on Phenotypes Enhances Weight Loss: A Pragmatic Trial in an Obesity Clinic. Obesity (Silver Spring). 2021;29(4):662-671
3. Stierman, B, Afful, J, Carroll, MD et al. National Health and Nutrition Examination Survey 2017–March 2020 Prepandemic Data Files Development of Files and Prevalence Estimates for Selected Health Outcomes. National Health Statistics Reports. Series : NHSR No. 158.

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