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Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

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Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

Curators:

• Cardiovascular Expertise – Dr. Justin D. Pearlman, MD, PhD, FACC
• Pharmacology and Oncology Expertise – Dr. Stephen J. Williams, PhD
• Principal Investigator, initiator of the project – Aviva Lev-Ari, PhD, RN

 

THE VOICE of Aviva Lev-Ari, PhD, RN

In this curation we wish to present two breaking through goals:

Goal 1:

Exposition of a new direction of research leading to a more comprehensive understanding of Metabolic Dysfunctional Diseases that are implicated in effecting the emergence of the two leading causes of human mortality in the World in 2023: (a) Cardiovascular Diseases, and (b) Cancer

Goal 2:

Development of Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics for these eight subcellular causes of chronic metabolic diseases. It is anticipated that it will have a potential impact on the future of Pharmaceuticals to be used, a change from the present time current treatment protocols for Metabolic Dysfunctional Diseases.

According to Dr. Robert Lustig, M.D, an American pediatric endocrinologist. He is Professor emeritus of Pediatrics in the Division of Endocrinology at the University of California, San Francisco, where he specialized in neuroendocrinology and childhood obesity, there are eight subcellular pathologies that drive chronic metabolic diseases.

These eight subcellular pathologies can’t be measured at present time.

In this curation we will attempt to explore methods of measurement for each of these eight pathologies by harnessing the promise of the emerging field known as Bioelectronics.

Unmeasurable eight subcellular pathologies that drive chronic metabolic diseases

1. Glycation
2. Oxidative Stress
3. Mitochondrial dysfunction [beta-oxidation Ac CoA malonyl fatty acid]
4. Insulin resistance/sensitive [more important than BMI], known as a driver to cancer development
5. Membrane instability
6. Inflammation in the gut [mucin layer and tight junctions]
7. Epigenetics/Methylation
8. Autophagy [AMPKbeta1 improvement in health span]

Diseases that are not Diseases: no drugs for them, only diet modification will help

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

Exercise will not undo Unhealthy Diet

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

These eight Subcellular Pathologies driving Chronic Metabolic Diseases are becoming our focus for exploration of the promise of Bioelectronics for two pursuits:

1. Will Bioelectronics be deemed helpful in measurement of each of the eight pathological processes that underlie and that drive the chronic metabolic syndrome(s) and disease(s)?
2. IF we will be able to suggest new measurements to currently unmeasurable health harming processes THEN we will attempt to conceptualize new therapeutic targets and new modalities for therapeutics delivery – WE ARE HOPEFUL

In the Bioelecronics domain we are inspired by the work of the following three research sources:

1. Biological and Biomedical Electrical Engineering (B2E2) at Cornell University, School of Engineering https://www.engineering.cornell.edu/bio-electrical-engineering-0
2. Bioelectronics Group at MIT https://bioelectronics.mit.edu/
3. The work of Michael Levin @Tufts, The Levin Lab

Michael Levin is an American developmental and synthetic biologist at Tufts University, where he is the Vannevar Bush Distinguished Professor. Levin is a director of the Allen Discovery Center at Tufts University and Tufts Center for Regenerative and Developmental Biology. Wikipedia

Born: 1969 (age 54 years), Moscow, Russia

Education: Harvard University (1992–1996), Tufts University (1988–1992)

Affiliation: University of Cape Town

Research interests: Allergy, Immunology, Cross Cultural Communication

Awards: Cozzarelli prize (2020)

Doctoral advisor: Clifford Tabin

Fields: Developmental biology, synthetic biology

SOURCE

https://www.google.com/search?q=Michael+Levin+%40Tufts&oq=Michael+Levin+%40Tufts&aqs=chrome..69i57j69i58.894128314j0j15&sourceid=chrome&ie=UTF-8

Most recent 20 Publications by Michael Levin, PhD

SOURCE

https://facultyprofiles.tufts.edu/michael-levin-1/publications

SCHOLARLY ARTICLE

The nonlinearity of regulation in biological networks

1 Dec 2023npj Systems Biology and Applications9(1)

Co-authorsManicka S, Johnson K, Levin M…1 more

VIEW PDF10.1038/S41540-023-00273-W

3

SCHOLARLY ARTICLE

Toward an ethics of autopoietic technology: Stress, care, and intelligence

1 Sep 2023BioSystems231

Co-authorsWitkowski O, Doctor T, Solomonova E…2 more

VIEW PDF10.1016/J.BIOSYSTEMS.2023.104964

SCHOLARLY ARTICLE

Closing the Loop on Morphogenesis: A Mathematical Model of Morphogenesis by Closed-Loop Reaction-Diffusion

14 Aug 2023Frontiers in Cell and Developmental Biology11:1087650

Co-authorsGrodstein J, McMillen P, Levin M

VIEW PDF10.3389/FCELL.2023.1087650

SCHOLARLY ARTICLE

Correcting instructive electric potential patterns in multicellular systems: External actions and endogenous processes.

30 Jul 2023Biochim Biophys Acta Gen Subj1867(10):130440

Co-authorsCervera J, Levin M, Mafe S

VIEW MORE INFO10.1016/J.BBAGEN.2023.130440

SCHOLARLY ARTICLE

Regulative development as a model for origin of life and artificial life studies

1 Jul 2023BioSystems229

Co-authorsFields C, Levin M

10.1016/J.BIOSYSTEMS.2023.104927

1

SCHOLARLY ARTICLE

The Yin and Yang of Breast Cancer: Ion Channels as Determinants of Left–Right Functional Differences

1 Jul 2023International Journal of Molecular Sciences24(13)

Co-authorsMasuelli S, Real S, McMillen P…3 more

VIEW PDF10.3390/IJMS241311121

SCHOLARLY ARTICLE

Bioelectricidad en agregados multicelulares de células no excitables- modelos biofísicos

Jun 2023Revista Española de Física32(2)

Co-authorsCervera J, Levin M, Mafé S

SCHOLARLY ARTICLE

Bioelectricity: A Multifaceted Discipline, and a Multifaceted Issue!

1 Jun 2023Bioelectricity5(2):75

Co-authorsDjamgoz MBA, Levin M

10.1089/BIOE.2023.0022.EDITORIAL

SCHOLARLY ARTICLE

Control Flow in Active Inference Systems – Part I: Classical and Quantum Formulations of Active Inference

1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):235-245

Co-authorsFields C, Fabrocini F, Friston K…4 more

10.1109/TMBMC.2023.3272150

2

SCHOLARLY ARTICLE

Control Flow in Active Inference Systems – Part II: Tensor Networks as General Models of Control Flow

1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):246-256

Co-authorsFields C, Fabrocini F, Friston K…4 more

10.1109/TMBMC.2023.3272158

2

SCHOLARLY ARTICLE

Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology

1 Jun 2023Cellular and Molecular Life Sciences80(6)

Co-authorsLevin M

VIEW PDF10.1007/S00018-023-04790-Z

1

SCHOLARLY ARTICLE

Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging

1 Jun 2023Drug Discovery Today28(6)

Co-authorsPio-Lopez L, Levin M

VIEW PDF10.1016/J.DRUDIS.2023.103585

1

SCHOLARLY ARTICLE

Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging

Jun 2023DRUG DISCOVERY TODAY28(6):1-13

Co-authorsPio-Lopez L, Levin M

VIEW MORE INFO10.1016/J.DRUDIS.2023.103585THIS

SCHOLARLY ARTICLE

Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine

12 May 2023Patterns4(5)

Co-authorsMathews J, Chang A, Devlin L…1 more

VIEW PDF10.1016/J.PATTER.2023.100737

3

SCHOLARLY ARTICLE

Making and breaking symmetries in mind and life

14 Apr 2023Interface Focus13(3)

Co-authorsSafron A, Sakthivadivel DAR, Sheikhbahaee Z…3 more

VIEW PDF10.1098/RSFS.2023.0015

SCHOLARLY ARTICLE

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

14 Apr 2023Interface Focus13(3)

Co-authorsPio-Lopez L, Bischof J, LaPalme JV…1 more

VIEW PDF10.1098/RSFS.2022.0072

1

SCHOLARLY ARTICLE

The collective intelligence of evolution and development

Apr 2023Collective Intelligence2(2):263391372311683SAGE Publications

Co-authorsWatson R, Levin M

VIEW PDF10.1177/26339137231168355

SCHOLARLY ARTICLE

Bioelectricity of non-excitable cells and multicellular pattern memories: Biophysical modeling

13 Mar 2023Physics Reports1004:1-31

Co-authorsCervera J, Levin M, Mafe S

10.1016/J.PHYSREP.2022.12.003

2

SCHOLARLY ARTICLE

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

1 Mar 2023Biomimetics8(1)

Co-authorsBongard J, Levin M

VIEW PDF10.3390/BIOMIMETICS8010110

4

SCHOLARLY ARTICLE

Transplantation of fragments from different planaria: A bioelectrical model for head regeneration

7 Feb 2023Journal of Theoretical Biology558

Co-authorsCervera J, Manzanares JA, Levin M…1 more

VIEW PDF10.1016/J.JTBI.2022.111356

SCHOLARLY ARTICLE

Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind

1 Jan 2023Animal Cognition

Co-authorsLevin M

VIEW PDF10.1007/S10071-023-01780-3

2

SCHOLARLY ARTICLE

Biological Robots: Perspectives on an Emerging Interdisciplinary Field

1 Jan 2023Soft Robotics

Co-authorsBlackiston D, Kriegman S, Bongard J…1 more

10.1089/SORO.2022.0142

1

SCHOLARLY ARTICLE

Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model

1 Jan 2023Entropy25(1)

Co-authorsShreesha L, Levin M

VIEW PDF10.3390/E25010131

5

SCHOLARLY ARTICLE

Endless forms most beautiful 2.0: teleonomy and the bioengineering of chimaeric and synthetic organisms (July, blac073, 2022)

1 Jan 2023BIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY138(1):141

Co-authorsClawson WP, Levin M

VIEW PDFVIEW MORE INFO10.1093/BIOLINNEAN/BLAC116

SCHOLARLY ARTICLE

Future medicine: from molecular pathways to the collective intelligence of the body

1 Jan 2023Trends in Molecular Medicine

Co-authorsLagasse E, Levin M

10.1016/J.MOLMED.2023.06.007

THE VOICE of Dr. Justin D. Pearlman, MD, PhD, FACC

PENDING

THE VOICE of  Stephen J. Williams, PhD

Ten TakeAway Points of Dr. Lustig’s talk on role of diet on the incidence of Type II Diabetes

 

1. 25% of US children have fatty liver
2. Type II diabetes can be manifested from fatty live with 151 million  people worldwide affected moving up to 568 million in 7 years
3. A common myth is diabetes due to overweight condition driving the metabolic disease
4. There is a trend of ‘lean’ diabetes or diabetes in lean people, therefore body mass index not a reliable biomarker for risk for diabetes
5. Thirty percent of ‘obese’ people just have high subcutaneous fat.  the visceral fat is more problematic
6. there are people who are ‘fat’ but insulin sensitive while have growth hormone receptor defects.  Points to other issues related to metabolic state other than insulin and potentially the insulin like growth factors
7. At any BMI some patients are insulin sensitive while some resistant
8. Visceral fat accumulation may be more due to chronic stress condition
9. Fructose can decrease liver mitochondrial function
10. A methionine and choline deficient diet can lead to rapid NASH development

 

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#TUBiol5227: Biomarkers & Biotargets: Genetic Testing and Bioethics

Posted in and Bioethics, Bio-Ethics, BioBanking, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biomarkers & Medical Diagnostics, BioSimilars, Biotechnology, BioTechnology - Venture Creation, Cancer and Current Therapeutics, Cancer Genomics, Clinical Diagnostics, Clinical Genomics, Conference Coverage with Social Media, Ethics and Leadership, FDA, FDA Regulatory Affairs, Federal Budget Appropriations, Health Economics and Outcomes Research, Health Law Policy, Healthcare costs and reimbursement, Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough, interventional oncology, Next Generation Sequencing (NGS), Patent Law in Biotech, Patents, Patient Experience, Personal Health Applications: Tech Innovations serves HealhCare, Personalized and Precision Medicine & Genomic Research, Personalized Medicine Coalition, Precision Cancer Medicine, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Voices of Patients and Healthcare Providers, tagged #TUBiol5227, bioethics, biomarkers, Drug development, FDA, healthcare, insurers, legal, Patient, payers, physician-patient, Regulatory on September 28, 2021| Leave a Comment »

#TUBiol5227: Biomarkers & Biotargets: Genetic Testing and Bioethics

Curator: Stephen J. Williams, Ph.D.

The advent of direct to consumer (DTC) genetic testing and the resultant rapid increase in its popularity as well as companies offering such services has created some urgent and unique bioethical challenges surrounding this niche in the marketplace. At first, most DTC companies like 23andMe and Ancestry.com offered non-clinical or non-FDA approved genetic testing as a way for consumers to draw casual inferences from their DNA sequence and existence of known genes that are linked to disease risk, or to get a glimpse of their familial background. However, many issues arose, including legal, privacy, medical, and bioethical issues. Below are some articles which will explain and discuss many of these problems associated with the DTC genetic testing market as well as some alternatives which may exist.

‘Direct-to-Consumer (DTC) Genetic Testing Market to hit USD 2.5 Bn by 2024’ by Global Market Insights

This post has the following link to the market analysis of the DTC market (https://www.gminsights.com/pressrelease/direct-to-consumer-dtc-genetic-testing-market). Below is the highlights of the report.

As you can see,this market segment appears to want to expand into the nutritional consulting business as well as targeted biomarkers for specific diseases.

Rising incidence of genetic disorders across the globe will augment the market growth

Increasing prevalence of genetic disorders will propel the demand for direct-to-consumer genetic testing and will augment industry growth over the projected timeline. Increasing cases of genetic diseases such as breast cancer, achondroplasia, colorectal cancer and other diseases have elevated the need for cost-effective and efficient genetic testing avenues in the healthcare market.
 

For instance, according to the World Cancer Research Fund (WCRF), in 2018, over 2 million new cases of cancer were diagnosed across the globe. Also, breast cancer is stated as the second most commonly occurring cancer. Availability of superior quality and advanced direct-to-consumer genetic testing has drastically reduced the mortality rates in people suffering from cancer by providing vigilant surveillance data even before the onset of the disease. Hence, the aforementioned factors will propel the direct-to-consumer genetic testing market overt the forecast timeline.
 

Get more details on this report – Request Free Sample PDF
 

Nutrigenomic Testing will provide robust market growth

The nutrigenomic testing segment was valued over USD 220 million market value in 2019 and its market will witness a tremendous growth over 2020-2028. The growth of the market segment is attributed to increasing research activities related to nutritional aspects. Moreover, obesity is another major factor that will boost the demand for direct-to-consumer genetic testing market.
 

Nutrigenomics testing enables professionals to recommend nutritional guidance and personalized diet to obese people and help them to keep their weight under control while maintaining a healthy lifestyle. Hence, above mentioned factors are anticipated to augment the demand and adoption rate of direct-to-consumer genetic testing through 2028.
 

Browse key industry insights spread across 161 pages with 126 market data tables & 10 figures & charts from the report, “Direct-To-Consumer Genetic Testing Market Size By Test Type (Carrier Testing, Predictive Testing, Ancestry & Relationship Testing, Nutrigenomics Testing), By Distribution Channel (Online Platforms, Over-the-Counter), By Technology (Targeted Analysis, Single Nucleotide Polymorphism (SNP) Chips, Whole Genome Sequencing (WGS)), Industry Analysis Report, Regional Outlook, Application Potential, Price Trends, Competitive Market Share & Forecast, 2020 – 2028” in detail along with the table of contents:
https://www.gminsights.com/industry-analysis/direct-to-consumer-dtc-genetic-testing-market
 

Targeted analysis techniques will drive the market growth over the foreseeable future

Based on technology, the DTC genetic testing market is segmented into whole genome sequencing (WGS), targeted analysis, and single nucleotide polymorphism (SNP) chips. The targeted analysis market segment is projected to witness around 12% CAGR over the forecast period. The segmental growth is attributed to the recent advancements in genetic testing methods that has revolutionized the detection and characterization of genetic codes.
 

Targeted analysis is mainly utilized to determine any defects in genes that are responsible for a disorder or a disease. Also, growing demand for personalized medicine amongst the population suffering from genetic diseases will boost the demand for targeted analysis technology. As the technology is relatively cheaper, it is highly preferred method used in direct-to-consumer genetic testing procedures. These advantages of targeted analysis are expected to enhance the market growth over the foreseeable future.
 

Over-the-counter segment will experience a notable growth over the forecast period

The over-the-counter distribution channel is projected to witness around 11% CAGR through 2028. The segmental growth is attributed to the ease in purchasing a test kit for the consumers living in rural areas of developing countries. Consumers prefer over-the-counter distribution channel as they are directly examined by regulatory agencies making it safer to use, thereby driving the market growth over the forecast timeline.
 

Favorable regulations provide lucrative growth opportunities for direct-to-consumer genetic testing

Europe direct-to-consumer genetic testing market held around 26% share in 2019 and was valued at around USD 290 million. The regional growth is due to elevated government spending on healthcare to provide easy access to genetic testing avenues. Furthermore, European regulatory bodies are working on improving the regulations set on the direct-to-consumer genetic testing methods. Hence, the above-mentioned factors will play significant role in the market growth.
 

Focus of market players on introducing innovative direct-to-consumer genetic testing devices will offer several growth opportunities

Few of the eminent players operating in direct-to-consumer genetic testing market share include Ancestry, Color Genomics, Living DNA, Mapmygenome, Easy DNA, FamilytreeDNA (Gene By Gene), Full Genome Corporation, Helix OpCo LLC, Identigene, Karmagenes, MyHeritage, Pathway genomics, Genesis Healthcare, and 23andMe. These market players have undertaken various business strategies to enhance their financial stability and help them evolve as leading companies in the direct-to-consumer genetic testing industry.
 

For example, in November 2018, Helix launched a new genetic testing product, DNA discovery kit, that allows customer to delve into their ancestry. This development expanded the firm’s product portfolio, thereby propelling industry growth in the market.

The following posts discuss bioethical issues related to genetic testing and personalized medicine from a clinicians and scientisit’s perspective

Question: Each of these articles discusses certain bioethical issues although focuses on personalized medicine and treatment. Given your understanding of the robust process involved in validating clinical biomarkers and the current state of the DTC market, how could DTC testing results misinform patients and create mistrust in the physician-patient relationship?

Personalized Medicine, Omics, and Health Disparities in Cancer:  Can Personalized Medicine Help Reduce the Disparity Problem?

Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?

The following posts discuss the bioethical concerns of genetic testing from a patient’s perspective:

Ethics Behind Genetic Testing in Breast Cancer: A Webinar by Laura Carfang of survivingbreastcancer.org

Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

23andMe Product can be obtained for Free from a new app called Genes for Good: UMich’s Facebook-based Genomics Project

Question: If you are developing a targeted treatment with a companion diagnostic, what bioethical concerns would you address during the drug development process to ensure fair, equitable and ethical treatment of all patients, in trials as well as post market?

Articles on Genetic Testing, Companion Diagnostics and Regulatory Mechanisms

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Real Time Coverage @BIOConvention #BIO2019: Genome Editing and Regulatory Harmonization: Progress and Challenges

New York Times vs. Personalized Medicine? PMC President: Times’ Critique of Streamlined Regulatory Approval for Personalized Treatments ‘Ignores Promising Implications’ of Field

Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Protecting Your Biotech IP and Market Strategy: Notes from Life Sciences Collaborative 2015 Meeting

Question: What type of regulatory concerns should one have during the drug development process in regards to use of biomarker testing? From the last article on Protecting Your IP how important is it, as a drug developer, to involve all payers during the drug development process?

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Yet another Success Story: Machine Learning to predict immunotherapy response

Posted in Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Biomarkers & Medical Diagnostics, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer-Immune Interactions, Glioblastoma, Immuno-Oncology & Genomics, Immunology, Immunotherapy, Machine Learning, RNA Biology, Cancer and Therapeutics, tumor microenvironment, tagged biomarkers, Cancer immunotherapy, cell-cell interactions, computational algorithms, immune inhibitory checkpoints, immuno-oncology therapeutics, Machine Learning models, mathematical model, PDL1/PD1 pathway, RNA sequencing, transcription factors, tumor microenvironment on July 6, 2021| Leave a Comment »

Yet another Success Story: Machine Learning to predict immunotherapy response

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Immune-checkpoint blockers (ICBs) immunotherapy appears promising for various cancer types, offering a durable therapeutic advantage. Only a number of cases with cancer respond to this therapy. Biomarkers are required to adequately predict the responses of patients. This article evaluates this issue utilizing a system method to characterize the immune response of the anti-tumor based on the entire tumor environment. Researchers build mechanical biomarkers and cancer-specific response models using interpretable machine learning that predict the response of patients to ICB.

The lymphatic and immunological systems help the body defend itself by combating. The immune system functions as the body’s own personal police force, hunting down and eliminating pathogenic baddies.

According to Federica Eduati, Department of Biomedical Engineering at TU/e, “The immune system of the body is quite adept at detecting abnormally behaving cells. Cells that potentially grow into tumors or cancer in the future are included in this category. Once identified, the immune system attacks and destroys the cells.”

Immunotherapy and machine learning are combining to assist the immune system solve one of its most vexing problems: detecting hidden tumorous cells in the human body.

It is the fundamental responsibility of our immune system to identify and remove alien invaders like bacteria or viruses, but also to identify risks within the body, such as cancer. However, cancer cells have sophisticated ways of escaping death by shutting off immune cells. Immunotherapy can reverse the process, but not for all patients and types of cancer. To unravel the mystery, Eindhoven University of Technology researchers used machine learning. They developed a model to predict whether immunotherapy will be effective for a patient using a simple trick. Even better, the model outperforms conventional clinical approaches.

The outcomes of this research are published on 30th June, 2021 in the journal Patterns in an article entitled “Interpretable systems biomarkers predict response to immune-checkpoint inhibitors”.

The Study

• Characterization of the tumor microenvironment from RNAseq and prior knowledge
• Multi-task machine-learning models for predicting antitumor immune responses
• Identification of cancer-type-specific, interpretable biomarkers of immune responses
• EaSIeR is a tool to predict biomarker-based immunotherapy response from RNA-seq

“Tumor also contains multiple types of immune and fibroblast cells which can play a role in favor of or anti-tumor, and communicates among themselves,” said Oscar Lapuente-Santana, a researcher doctoral student in the computational biology group. “We had to learn how complicated regulatory mechanisms in the micro-environment of the tumor affect the ICB response. We have used RNA sequencing datasets to depict numerous components of the Tumor Microenvironment (TME) in a high-level illustration.”

Using computational algorithms and datasets from previous clinical patient care, the researchers investigated the TME.

Eduati explained

While RNA-sequencing databases are publically available, information on which patients responded to ICB therapy is only available for a limited group of patients and cancer types. So, to tackle the data problem, we used a trick.

All 100 models learned in the randomized cross-validation were included in the EaSIeR tool. For each validation dataset, we used the corresponding cancer-type-specific model: SKCM for the melanoma Gide, Auslander, Riaz, and Liu cohorts; STAD for the gastric cancer Kim cohort; BLCA for the bladder cancer Mariathasan cohort; and GBM for the glioblastoma Cloughesy cohort. To make predictions for each job, the average of the 100 cancer-type-specific models was employed. The predictions of each dataset’s cancer-type-specific models were also compared to models generated for the remaining 17 cancer types.

From the same datasets, the researchers selected several surrogate immunological responses to be used as a measure of ICB effectiveness.

Lapuente-Santana stated

One of the most difficult aspects of our job was properly training the machine learning models. We were able to fix this by looking at alternative immune responses during the training process.

Some of the researchers employed the machine learning approach given in the paper to participate in the “Anti-PD1 Response Prediction DREAM Challenge.”

DREAM is an organization that carries out crowd-based tasks with biomedical algorithms. “We were the first to compete in one of the sub-challenges under the name cSysImmunoOnco team,” Eduati remarks.

The researchers noted,

We applied machine learning to seek for connections between the obtained system-based attributes and the immune response, estimated using 14 predictors (proxies) derived from previous publications. We treated these proxies as individual tasks to be predicted by our machine learning models, and we employed multi-task learning algorithms to jointly learn all tasks.

The researchers discovered that their machine learning model surpasses biomarkers that are already utilized in clinical settings to evaluate ICB therapies.

But why are Eduati, Lapuente-Santana, and their colleagues using mathematical models to tackle a medical treatment problem? Is this going to take the place of the doctor?

Eduati explains

Mathematical models can provide an overview of the interconnection between individual molecules and cells and at the same time predicting a particular patient’s tumor behavior. This implies that immunotherapy with ICB can be personalized in a patient’s clinical setting. The models can aid physicians with their decisions about optimum therapy, it is vital to note that they will not replace them.

Furthermore, the model aids in determining which biological mechanisms are relevant for the biological response.

The researchers noted

Another advantage of our concept is that it does not need a dataset with known patient responses to immunotherapy for model training.

Further testing is required before these findings may be implemented in clinical settings.

Main Source:

Lapuente-Santana, Ó., van Genderen, M., Hilbers, P. A., Finotello, F., & Eduati, F. (2021). Interpretable systems biomarkers predict response to immune-checkpoint inhibitors. Patterns, 100293. https://www.cell.com/patterns/pdfExtended/S2666-3899(21)00126-4

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Dysregulation of ncRNAs in association with Neurodegenerative Disorders

Posted in Alzheimer's Disease, Biomarkers & Medical Diagnostics, Cerebrovascular and Neurodegenerative Diseases, Etiology, Molecular Genetics & Pharmaceutical, mRNA, mRNA Therapeutics, Neurodegenerative Diseases, Neurological Diseases, RNA Biology, RNA interference (RNAi) therapeutic, tagged Alzheimers Disease, neurodegenerative disorders, Neurological Disorders, Non-coding RNA, Parkinsons disease, RNA Biology, RNA interference (RNAi) therapeutic on January 11, 2021| Leave a Comment »

Dysregulation of ncRNAs in association with Neurodegenerative Disorders

Curator: Amandeep Kaur

Research over the years has added evidences to the hypothesis of “RNA world” which explains the evolution of DNA and protein from a simple RNA molecule. Our understanding of RNA biology has dramatically changed over the last 50 years and rendered the scientists with the conclusion that apart from coding for protein synthesis, RNA also plays an important role in regulation of gene expression.

The universe of non-coding RNAs (ncRNAs) is transcending the margins of preconception and altered the traditional thought that the coding RNAs or messenger RNAs (mRNAs) are more prevalent in our cells. Research on the potential use of ncRNAs in therapeutic relevance increased greatly after the discovery of RNA interference (RNAi) and provided important insights into our further understanding of etiology of complex disorders.

Latest research on neurodegenerative disorders has shown the perturbed expression of ncRNAs which provides the functional association between neurodegeneration and ncRNAs dysfunction. Due to the diversity of functions and abundance of ncRNAs, they are classified into Housekeeping RNAs and Regulatory ncRNAs.

The best known classes of ncRNAs are the microRNAs (miRNAs) which are extensively studied and are of research focus. miRNAs are present in both intronic and exonic regions of matured RNA (mRNA) and are crucial for development of CNS. The reduction of Dicer-1, a miRNA biogenesis-related protein affects neural development and the elimination of Dicer in specifically dopaminergic neurons causes progressive degeneration of these neuronal cells in striatum of mice.

A new class of regulatory ncRNAs, tRNAs-derived fragments (tRFs) is superabundantly present in brain cells. tRFs are considered as risk factors in conditions of neural degeneration because of accumulation with aging. tRFs have heterogenous functions with regulation of gene expression at multiple layers including regulation of mRNA processing and translation, inducing the activity of silencing of target genes, controlling cell growth and differentiation processes.

The existence of long non-coding RNAs (lncRNAs) was comfirmed by the ENCODE project. Numerous studies reported that approximately 40% of lncRNAs are involved in gene expression, imprinting and pluripotency regulation in the CNS. lncRNA H19 is of paramount significance in neural viability and contribute in epilepsy condition by activating glial cells. Other lncRNAs are highly bountiful in neurons including Evf2 and MALAT1 which play important function in regulating neural differentiation and synapse formation and development of dendritic cells respectively.

Recently, a review article in Nature mentioned about the complex mechanisms of ncRNAs contributing to neurodegenerative conditions. The ncRNA-mediated mechanisms of regulation are as follows:

• Epigenetic regulation: Various lncRNAs such as BDNF-AS, TUG1, MEG3, NEAT1 and TUNA are differentially expressed in brain tissue and act as epigenetic regulators.

• RNAi: RNA interference includes post-transcriptional repression by small-interfering RNAs (siRNAs) and binding of miRNAs to target genes. In a wide spectrum of neurodegenerative diseases such as Alzheimer’s disease, Parkinson disease, Huntington’s disease, Amyotrophic lateral sclerosis, Fragile X syndrome, Frontotemporal dementia, and Spinocerebellar ataxia, have shown perturbed expression of miRNA.

• Alternative splicing: Variation in splicing of transcripts of ncRNAs has shown adverse affects in neuropathology of degenerative diseases.

• mRNA stability: The stability of mRNA may be affected by RNA-RNA duplex formation which leads to the degradation of sense mRNA or blocking the access to proteins involved in RNA turnover and modify the progression of neurodegenerative disorders.

• Translational regulation: Numerous ncRNAs including BC200 directly control the translational process of transcripts of mRNAs and effect human brain of Alzheimer’s disease.

• Molecular decoys: Non-coding RNAs (ncRNAs) dilute the expression of other RNAs by molecular trapping, also known as competing endogenous RNAs (ceRNAs) which hinder the normal functioning of RNAs. The ceRNAs proportion must be equivalent to the number of target miRNAs that can be sequestered by each ncRNAs in order to induce consequential de-repression of the target molecules.

The unknown functions of numerous annotated ncRNAs may explain the underlying complexity in neurodegenerative disorders. The profiling of ncRNAs of patients suffering from neurodevelopmental and neurodegenerative conditions are required to outline the changes in ncRNAs and their role in specific regions of brain and cells. Analysis of Large-scale gene expression and functional studies of ncRNAs may contribute to our understanding of these diseases and their remarkable connections. Therefore, targeting ncRNAs may provide effective therapeutic perspective for the treatment of neurodegenerative diseases.

References https://www.nature.com/scitable/topicpage/rna-functions-352/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6035743/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7695195/ https://link.springer.com/article/10.1007/s13670-012-0023-4 https://www.nature.com/articles/nrn.2017.90

 

Other related articles were published in this Open Access Online Scientific Journal, including the following:

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