2020 - SciWriteGlobal

Nov, 2020

Nanopore sequencing enables high-resolution analysis of resistance determinants and mobile elements in the human gut microbiome

High-resolution analysis of resistant determinants and mobile elements in the human gut microbiome is quite difficult with the existing technologies. However, in the present research study, the authors highlight the effectiveness of Nanopore sequencing in getting the desired results. The analysis of genomic data derived from mobile elements in the human gut microbiome can often be limited due to the fragmented assembly from short-read sequencing.

One can overcome the limitations of fragmented assembly by using third-generation sequencing technology, which helps in getting long-reads. However, it is noted that third-generation based sequencing technologies give high-error rates and very poor throughput rates. This has resulted in limited use of technology in metagenomic related studies. The researchers in the present study have found a new way to overcome the exiting challenges by developing the first hybrid metagenomic assembler which will combine the properties of both long and short-read technologies. This will surely be considered to give high improvement when compared with the older version of assemblies along with high base par accuracy.

The approach includes a metagenome clustering technique which will be unique. It will include a scaffolding algorithm that can repeat-rich sequences with high accuracy and low error rates. Based on the numerous analysis done, the researchers identified that near-complete genomes from metagenomes can be assembled with as little as 9x long read coverage. This can enable the high-quality assembly of less abundant species. To take this understanding further, the researchers applied the concept of nanopore sequencing to analyze the gut microbiome of patients under antibiotic treatment. It was found in the study that long reads can be obtained from the samples to create accurate and efficient assemblies.

Nov, 2020

A critical review on quantum dots: From synthesis toward applications in electrochemical biosensors for determination of disease-related biomolecules

Nanoscience or nanotechnology is a very fast-growing field of science, which has introduced various new transforming technology in the present era. One such technology is fluorescent quantum dots (QDs), which have been a part of nanotechnology since the very beginning of the field. Fluorescent quantum dots are very small nanocrystals, defines by a diameter of about 2-10 nanometers i.e. around 10-50 atoms. The most unique property of quantum dots is the resultant fluorescence of distinctive colors produced, which is highly dependent on the size of the nanocrystal.

Quantum dots are known to be consisting of a variety of structural, photochemical, and electrochemical properties also, which can be exploited to use them as a very promising technology in the field of sensing applications. The use of quantum dots as a nanomaterial in these sensing applications can increase the performance of biosensors in the market, specifically in terms of overcoming the existing issues such as detection limit, selectivity, and sensor sensitivity. The applications of quantum dots is not only limited to this, instead, it also expands to their high-level functionalization with bioreceptors. In this review article, the authors highlight how fluorescent quantum dots function and their core knowledge along with a detailed explanation of their applications in sensors to receptors.

The potential of quantum dots is immense, one of the reason is their enhanced capability to associate nanotechnology and biotechnology together. They indeed possess a huge potential to set a new paradigm in the research field to give a comprehensive view of zero-dimensional nanoparticles. These nanoparticles can be effectively used in the designing of electrochemical sensors which can be used in the diagnosis of diseases. This can specifically include identifying biomolecules such as tumor markers, depression markers, inflammatory markers, and more. Considering the huge application of quantum dots, the researcher highlights more in-depth research in the field. Detailed insight about quantum dots can help in understanding their electronic and magnetic properties in more detail. One can understand how they can be synthesized in labs efficiently for further large scale production to be used effectively at the industrial scale for biomolecule diagnosis and other related applications.

Nov, 2020

Silent Mutations and RNA folding can give answer, Why COVID became Unstoppable?

Silent Mutations and RNA folding can give answer, Why COVID-19 became Unstoppable?

Till now, it’s very evident that almost every person must be knowing about the lethal impact of COVID-19 across the globe. But hardly, anyone knows the reason that how COVID-19, which was once living harmlessly in wildlife crossed the species barrier, giving it an evolutionary edge.

Recently, scientists at Duke University have found out that there have been numerous silent mutations in the genetic code of the coronavirus which helped it get the evolutionary edge and made it thrive beyond wildlife, leading to a global pandemic. These silent mutations guided the folding of RNA molecules in a unique way when present in human cells, setting the stage for the global crisis. The study has been published in the journal Peer. J.

The study involved various statistical methods that help in identifying various adaptations that the virus underwent. Researchers analyzed the genome sequence of the SARS-CoV-2 virus and other related coronaviruses often found in bats and pangolins to find out the adaptive changes in SARS-CoV2. “We’re trying to figure out what made this virus so unique,” said Alejandro Berrio, lead author of the paper and researcher at Duke University.

The earlier research highlighted the presence of positive selection within a gene that is responsible for encoding “Spike protein” on the surface of SARS-CoV2 surface. This increases the ability of the virus to infect the new cells in humans more outrageously. The study also indicated the presence of certain mutations in the viral genome that must have altered the spike protein, which made it thrive more easily among humans leading to a global pandemic. But this is not enough, the researchers also found out various other aspects which were not studies in previous researches, highlighting the reason that COVID has become so lethal and infectious. One of the critical reasons was a silent mutation in the two very important regions of the SARS-CoV2 genome, also referred to as Nsp4 and Nsp16. These mutations have been considered to give COVID-19 an evolutionary edge when compared with other similar strains, without impacting the proteins they naturally encode.

The study helped in understanding that the silent mutation instead of affecting the resultant protein affected how the RNA folds up in 3D shape, which eventually affects its functions in human cells. The deeper insights about how exactly these mutations govern the changes I RNA structure is yet to be elucidated in more detail. But never the less, the present study has very well contributed to understanding the viral leap from wildlife to humans.

“Nsp4 and Nsp16 are among the first RNA molecules that are produced when the virus infects a new person,” Berrio said. “The spike protein doesn’t get expressed until later. So they could make a better therapeutic target because they appear earlier in the viral life cycle.”

“Viruses are constantly mutating and evolving,” Berrio said. “So it’s possible that a new strain of coronavirus capable of infecting other animals may come along that also has the potential to spread to people as SARS-CoV-2 did. We’ll need to be able to recognize it and make efforts to contain it early.”

Nov, 2020

Artificial Intelligence on its way to help to predict drug resistant bacteria

Researchers of Washington state university have developed a feasible software in their lab which helps to identify drug-resistant contributing genes in bacteria.

The software developed makes it feasible to use and easily identify the fatal anti-microbial resistant bacteria existing with us in nature. Figures show that anti-microbial resistance bacteria alone cause more than 2.8 million cases of deadly pneumonia, blood infection, and 35000 deaths annually. Researcher Abu Sayed- Doctoral graduate in computer science, Shira Broschat from School of Electrical Engineering and computer science, and Douglas Call from Paul G. Allen School for Global Animal Health have shared the insight of their work with the Scientific Reports journal.

Antimicrobial resistance is a process that is observed when a microbe acquires or evolves with a drug resistance gene thus further causing AMR. Bacteria responsible for staphylococcus or streptococcus infection or in that manner tuberculosis and pneumonia, all these infectious diseases have developed due to the occurrence of drug resistance strains thus causing hindrance in the treatment regime. The AMR issue is estimated to worsen more in the coming decades in terms of AMR-related infection, deaths, and an increase in health costs as the bacteria is evolving in a way where there is a limited option of antibiotic treatment against it.

“We need to develop tools to easily and efficiently predict antimicrobial resistance that increasingly threatens health and livelihoods around the world,” said Chowdhury, lead author of the paper.

With the ease of large-scale genetic sequencing, scientists are looking in the environment for the presence of AMR genes. They are interested to know the habitat of such genes and how potentially such AMR microbes can spread and affect human health. While they can identify genes that are similar to known AMR-resistant genes, they are probably missing genes for resistance that look very unique from a protein sequence perspective.

The team at WSU developed a machine learning algorithm that has a unique feature of having data of AMR proteins instead of using gene sequences to find a similarity and identify AMR genes. The above algorithm was developed with the help of a theory known as Game theory. Game theory is a tool used in various fields more precisely in economics, to model strategic interactions between game players, here, in this case, helps to identity AMR genes. Using algorithm and theory together, the researcher looked at the interaction of various aspects such as genetic material, structure and physicochemical properties, and properties of protein sequences rather than simply looking at sequence similarity.

“Our software can be employed to analyze metagenomic data in greater depth than would be achieved by simple sequence matching algorithms,” Chowdhury said. He further added, “This can be an important tool to identify novel antimicrobial resistance genes that eventually could become clinically important.”

“The virtue of this program is that we can actually detect AMR in newly sequenced genomes,” Broschat said. “It’s a way of identifying AMR genes and their prevalence that might not otherwise have been found. That’s really important.”

The pioneer research team at WSU took Clostridium, Enterococcus, Staphylococcus, Streptococcus, and Listeria species where resistance genes are found. The above-mentioned bacteria are one of the major causes of infections such as staph infection, food poising, life-threatening pneumonia, and colitis. The algorithm developed by the team was able to categories the resistance genes accurately up to 90 percent.

The algorithm is embedded as a software package that is downloadable easily and can also be used by the scientific fraternity to look into AMR in a large pool of genetic material. The software can be updated timely. As more sequences and data become available, researchers will be able to continue with the algorithm without any hassle. “You can bootstrap and improve the software as more positive data becomes available,” Broschat said.

Sep, 2020

Antibiotic Resistance- An ongoing crisis

Antibiotic resistance is an ancient and mounting problem. Common causes are overpopulation, increased use of antibiotics, and even due to enhanced global migration. For a long time, antibiotic treatment has been the main approach of treatment in modern medicine to combat infections. The golden era of antibiotics was between the 1930s to 1960s, giving rise to many antibiotics. Antibiotic resistance poses a very serious global threat due to the growing concern of human and animal health. This is due to multidrug-resistant bacteria or popularly known as “superbugs”.

The plausible causes of global microbial resistance include overuse of antibiotics in animals consumed as food by humans. Other superficial causes include, increase in international travel and poor hygiene. These factors mainly play a role in the genetic selection in a community of resisting antibiotics.

History and Benefits of Antibiotics:

The first documented use of antibiotics was with the discovery of penicillin by Alexander Fleming during the world war. Penciliine was successful in controlling the bacteria and was said to have saved millions of lives. Shortly after the boom in usage, the penicillin resistance was a major problem, threatening many of the advances in the medical field at that time. What was thought as just penicillin resistance later proved to be a multidrug resistance. Microorganisms under Darwinian natural selection to develop the resistance. Naturally, most antibiotics are produced using bacteria or environmental fungi, and few are completely synthetic using sulphonamides and fluoroquinolones.

Not only saving lives, but antibiotics have also played a vital role in advancing major breakthroughs in the field of surgery and medicine. They have successfully prevented and treated infections that commonly occur in patients undergoing chemotherapy, who are also suffering from chronic diabetes or rheumatoid arthritis.

Overuse:

Even as early as 1945, Alexander Fleming with the discovery of penicillin, also warned the public that the era of antibiotics will also lead to the era of misuse and resistance. Epidemiological studies depict a direct relationship between the consumption of antibiotics and the rise in bacterial resistant diseases. These bacteria can be transmitted or inherited between relatives and friends. Resistance can also occur due to spontaneous mutation. Antibiotics remove the sensitive competitors and leave the resistance bacteria by natural selection to reproduce and multiply more. Despite these warnings, antibiotics are overprescribed throughout the world.

Agricultural use:

In both developing and developed worlds, antibiotics are seen to be used very frequently as growth supplements in livestock. According to a census, almost 80% of the antibiotics sold throughout the world are used in animals to prevent infection and promote growth. Treating livestock with antimicrobials can improve overall health, produce larger yields, and an overall high-quality product.

The antibiotics used in treating livestock is in turn consumed by the top of the food chain- humans. This transfer of resistant bacteria to humans was first noticed 35 years ago when the antibiotic rates were found high in the intestines of both farm animals and farmers. Recently molecular detection methods prove that resistant bacteria from farm animals reach humans through consumption.

It should also be noted that the agricultural use of antibiotics also thrive in urine and stool. These are widely dispersed through fertilizers affecting the groundwater and surface. While this may account for a small fraction of overall antibiotic use, the end geographical spread can be a considerable size. This practice also affects the micro

The emergence of resistance:

Organisms develop resistance by several techniques including altering the target site of binding, inhibiting the drug entry, and enzyme production that leads to the degradation of antimicrobials.Various antimicrobial drugs like antibiotics produced by saprophytic bacteria tend to develop mutual benefits with the other organisms surrounding it and sometimes even inhibit growth. Available data suggest that the sublethal concentration is the antibiotics have a significant impact on the microbial flora, and may even be effective in signalling molecules which may induce a microbial and host gene expression.

Another important finding reveals that few saprophytic bacteria is capable of producing a broad-spectrum antibiotic known as carbapenems. Various genes involved in constructing this antibiotic may also play an important role in biofilm formation. These findings reveal more unexpected impacts of the antibiotics.

More knowledge is needed to the extent of the broad-spectrum antimicrobial resistance. Current panic is due to the inadequate information. The future cannot be predicted with surety at the stage with regards to the resistance with the unavailability of novel antibiotics. Multiple well thought strategies need to be in place to confront this particular issue. Regulations should be implemented by every country to monitor prescriptions and the use of antibiotics. Environmental and ecological issues should not be ignored and all elements should be part of the control policy. Alternatives to antibiotics such as lytic bacteriophages vectors and probiotics can potentially help to decrease the use of antibiotics.

Sep, 2020

COVID-19: Ridgeback working on anti-viral tests for patients in the hospital and at home

With efforts underway to find treatments for COVID-19 patients at the hospital, Ridgeback therapeutics are doing things differently.

By the looks of it, COVID-19 does not seem to slow down anytime soon. Thousands of fresh cases are being reported every day, and hospitals are flooded around the world. Most of the pharmaceutical companies are right now focused on finding a treatment for hospitalized patients, in a way that makes sense. After all, they are the most severely affected patients. But Miami based Ridgeback biotherapeutics are currently working differently.

Currently, the company is kicking off its phase 2 trial for an antiviral that can serve as a treatment for both hospitalized patients and freshly diagnosed patients who are staying at home. The underway antiviral- EIDD-2801 is first being tested on hospitalized COVID-19 patients and the second with cases at home.

What is EIDD-2801?

Although doctors and scientists around the world are testing out a variety of existing drugs to fight COVID-19, EIDD-2801, an oral antiviral drug stands apart.

The drug can be used as both prophylactic and a therapeutic against the COVID-19 virus. It is a nucleoside analog that shows broad spectrum activity against RNA viruses, which includes the current coronavirus, COVID-19 and previous virus MERS and SARS.

EIDD-2801 attacks RNA dependent RNA polymerase the same way as Gilead Sciences’ Remdesivir, a previously FDA-appointed drug for emergency use. But unlike remdesivir which is administered intravenously, EIDD-2801 can be taken orally.

Why EIDD-2801?

Since the convenience of taking EIDD-2801 orally, patients could take it at home rather than risking themselves and others by coming to the hospital. By this, the drug is taken at the earlier stages of the infection, potentially killing the virus before it causes havoc on the body.

EIDD-2801 is completely safe and effective and has a rather intriguing feature of being highly resistant. Usually drugs alert the viruses to quickly mutate that aren’t affected by the drug, making it incompetent. But EID-2801 when tested hasn’t prompted any such resistance.

“We always worry about resistance,” says Andy Mehle, a virologist at the University of Wisconsin–Madison. Though drug resistance is inevitable, sometimes the viruses work on changing so much to overcome a drug’s effects that they cripple. Alternatively, resistance may seem as a simple change, the changes occur with a difficulty of the virus’s ability to multiply. Experts speculate this might be the case with EIDD-2801.

What’s next for Ridgeback?

Though the first set of clinical studies are just beginning, Ridgeback is confident about the drug and is currently gearing up to manufacture hundreds of thousands of doses of the drug. In the near future the company is planning to ramp up the production to millions.

“While we still need to wait and see the intended efficacy of the drug as ridgeback believes it to be, it is imperative to have a backup of immediate and ample supply to the world, once the clinical trials are successful” stated Wendy Holman, CEO and co-founder of Ridgeback.

Sep, 2020

Good news as researchers find a key to cystic fibrosis detection and treatment

Groundbreaking research by Monash University paves the way to a possibility of better monitoring and treatment of the cystic fibrosis lung disease

For years, cystic fibrosis (CF) has known to be a serious hereditary condition that causes severe damage to the respiratory and digestive systems. The damage starts with a build-up of thick mucosal kind of fluid in the organs especially lungs.

In particular cystic fibrosis affects the cells that produce digestive enzymes, sweat, and mucus. Usually, these secretions are thin and watery, which function as lubrication for various organs and tissues. However, when affected with CF, due to faulty genes the person experiences thick fluid build-up, clogging up the passages and ducts.

Early diagnosis and treatment are important for improving the quality and the length of the affected life. Currently available lung assessment tools have many drawbacks, especially the inability to accurately identify the origin of the changes seen in lung health.

Monash University research team announced the results of the World’s first research promising a possibility of better diagnosis and treatment of cystic fibrosis using X-ray velocimetry.

What is X-ray velocimetry?

A phase-contrast X-ray imaging makes use of the refractive properties of materials to produce high definition soft tissue images. Recently X-ray velocimetry (XV) has caught the eye of researchers. In particular, XV is used to study the airflow through the lungs and the technique is based on particle image velocimetry. Particle image velocimetry (PIV) is a well-established technique known for years.

XV is known to provide high quality, non-invasive, real-time images of the airflow through the lungs. The X-ray was first designed and developed by 4DMedical, in hope for clinical use. The technology has also recently been approved by the FDA for its all respiratory indications in adults.

All about the study:

An effective assessment of the lung cystic fibrosis should be capable of capturing its patchy nature and fluid buildup. This is in particular important for detecting the disease at early stages.

With the help of XV, a multidisciplinary collaboration of engineers, physicists, and clinicians were able to measure real-time airflow through the lungs.

The research was led by one of the University’s leading scientists Dr. Freda Werdiger. The study revealed that by using XV, it was possible to pinpoint the precise locations where there was an obstruction of airflow in the lung of a cystic fibrosis patient.

“In this study, we present two developments in XV analysis. Firstly, we show the ability of laboratory-based XV to detect the patchy nature of CF-like disease in affected mice. Secondly, we present a technique for numerical quantification of that disease, which can delineate between two major modes of disease symptoms,” Dr. Werdiger said.

This particular model provides a very simple, easy top interpret approach, which in the future can be readily applied to large quantities of data generated in XV imaging.

What does the future hold?

The success of XV lies in its capability of drawing reliable and quantitative measures, and the above study shows how that can be accomplished.

The researchers of the study recommend that this promising technique should be applied to the numerical characterization of CF lung disease. The analysis can be applied in a straight forward fashion with minimal manual labour required.

Sep, 2020

Use of Computational Methods in Stem Cell Biology

For a few decades now, the field of developmental biology has utilized computational technologies to explore the mechanisms of the developmental process. It was first in the 1950’s Alan Turing wrote a computer program that was able to model how morphogen concentrations can affect the growth of an in vitro embryo. Since then several techniques have been developed that can generate comprehensive data of a molecular type also known as OMICs.

Though the use of computational methods was largely limited to the theoretical mechanisms, the birth of large genome sequencing, paved the way to process large molecular data. Computational models complement the statistical data by providing mechanistic insights into the biological processes and by the ability to predict future outcomes in terms of biological processes that can guide experimental research.

Difference between Mechanistic models and Machine Learning Models:

For several years, Machine Learning (ML) approaches have been used for pattern recognition, prediction, and classification of biological systems, especially system cell research. Some of the important examples include the construction of 3D stem cell images from fluorescent microscopic results. Ml can also predict the experimental conditions and determine future outcomes.

Although ML has a decent accurate predictive power, they require large amounts of data especially imaging and omics datasets for interpreting statistical relationships between the input and predicted output data. ML usually specializes in predicting the outcome but not revealing the underlying complex processes, preventing them from providing any mechanistic insights on the biological processes. ML can be classified as supervised and unsupervised learning. The supervised learning can predict outcomes of foreseen data by studying the labeled training data, whereas unsupervised tries to make sense of any unlabelled data by extracting in-depth features and patterns of its own.

By contrast, mechanistic models generally rely on the mechanistic hypothesis implied from the experimental data to predict novel outcomes and describe the behaviors of the whole system. These models are often assembled based on the simplified mathematical and conceptual formulations of the observed experiment. Moreover, a single based cell experiment of this model has been developed to elucidate cell fate dysregulation linked with congenital diseases.

Applications of Computational Methods in Regenerative Medicine

It is well known that cell transplantation especially using induced pluripotent stem cells (iPSCs) is one of the main strategies in regenerative medicine to reinstate damaged or ill-functioning cells. Though various clinical applications using iPSCs are underway there are still few challenges that need to be overcome before it reaches its full potential. One of the main ways to overcome this problem is by figuring out the in-vitro manufacturing of the donor cells to gain appropriate knowledge of the cell expression- the identity of host tissue cells.

Current techniques have a low conversion efficiency, forcing the researchers to spend a large number of their resources in order to get an accurate result. Moreover, cell conversion often results in creating unnecessary immature cells or non-variants of the cells, ultimately failing to reciprocate the desired functionalities and phenotypes. On the other hand, computational methods can help in achieving the desired results. The latest advancements in scRNA sequencing technologies can help the researchers to accurately characterize functionally gene expression and cell subtypes.

By combining the computational methods with existing novel experimental techniques, it is possible for researchers to now open up to new avenues in designing protocols and treatments for congenital disorders and for enhancing regeneration of cells.

Stem cell rejuvenation is another strategy promising to prevent the damaged stem cell function and to help optimize tissue repair processes in age-related or degenerative disorders. The main reason behind impaired stem cell function is the disruption of pathways of the endogenous stem cells due to certain mutations or aged niche. The computational models can help in determining this particular impaired niches and signaling pathways and further help in proving insights with the mechanisms of the cell dysregulation in aging. Researches can use these predicted signaling molecules to counteract a niche effect for rejuvenating stem cells.

Future Perspectives:

As discussed a number of challenges in the research can be resolved with the development of multiscale computational methods. With the increasing work in single-cell expansion and scRNA data, it is now possible to develop complex computational methods, including cell-cell communication and intracellular network-based models.

Although ML has been employed successfully in pattern recognition and classification, they are not capable of providing information on biological processes. The implementation of mechanistic models with ML can help in a better understanding of mechanisms and predictions based on simple assumptions. In the future, stem cell researchers could coordinate with computational models, before performing an experiment to address certain biological questions and assess the required data for the model.

Sep, 2020

Serum- and glucocorticoid- inducible kinase 2, SGK2, is a novel autophagy regulator and modulates platinum drugs response in cancer cells

Many cases of ovarian cancers arise from the epithelial cells of the ovary and fallopian tube. The epithelial ovarian cancer is not a single entity disease but rather are several subtypes, each with its distinct genetic and biological backgrounds. This diversity determines the clinical outcome of the disease, where the patients respond differently to the same treatment and sometimes even different prognosis.

For the past three decades, the standard treatment for advanced epithelial ovarian cancer is chemotherapy, commonly used platinum-based drugs (PT). Though the majority of the patients achieve complete remission, there are cases who might experience recurrence due to acquired resistance to platinum-based drugs. Cellular diversity in tumors and the microenvironment can lead to chemoresistance. Overcoming PT resistance is one of the major challenges faced in ovarian cancer research.

Over the years, many experiments have been conducted to identify the particular genes responsible for the mechanism directly associated with the PT resistance. PT resistance is linked to several alterations such as drug inactivation, transport, DNA repair, and apoptosis. Among the general pathways, researchers have also observed autophagy has shown to confer with the metabolic plasticity which is necessary to grow and survive in therapy-induced stress.

Autophagy is a dynamic catabolic process that aids in the formation of double-membrane vesicles also known as autophagosomes. They help in engulfing cellular proteins and organelles and deliver them to the lysosome. When autophagosomes merge with lysosomes, the contents are degraded and help in fueling the metabolic pathways.

In this study, the authors began a mission to find the genes responsible for the PT- chemoresistance. A loss of function screening was performed and unveiled that serum- glucocorticoid- kinase 2 (SGK2) as a novel modulator of platinum-based drug sensitivity. SGK family constitutes three isoforms: SGK1, SGK2, and SGK3. Most of the studies revolving around the SGK family, mention its role in the development of human diseases in cancer and in cellular physiology. SGKs were initially identified as regulators, and pumps in the context of epithelial cells ion transport. The study further demonstrates the previously unrecognized role of SGK2 in platinum based drug sensitivity exerted by the autophagic reflux.

Starting from identifying SGK2 as a druggable modulator, the study characterized the role of autophagy as an escaping strategy activated by the cancer cells to resist PT treatment. The study later demonstrated that pharmacological or genetic inhibition of the isotype SGK2 could potentially block the autophagic process stimulated by the PT treatment. This evidence throws light on the possibility of developing new anticancer strategies on drug repositioning. Overall the study proves that SGK2 kinase controls the PT induced cell death in epithelial ovarian cancer by inhibiting autophagy.

Sep, 2020

Isolation of potent SARS-CoV-2 neutralizing antibodies and protection from disease in a small animal model

For the past few months, there has been a global spread and toll of COVID-19. So far, humanity has been able to eradicate only one other human infectious disease- smallpox. The novel infectious disease- COVID-19 has had its devastating share of lives globally and currently there is no cure or licensed vaccine.

Many studies lately have been discussing in-depth about neutralizing antibodies. They represent therapeutic and prophylactic options that could help guide potential vaccine designs. Neutralizing antibodies (nAbs) in terms of another respiratory virus- respiratory syncytial virus (RSV) is widely used clinically, usually to protect vulnerable infants prophylactically. Generally, nAbs with good potency also known as super antibodies can supersize antiviral therapeutic efficiency. Along with the help of bioengineering, the nAbs half-life can be prolonged bringing down the cost considerably.

In this study, the authors try to present potent nAbs to the COVID-19 virus and further demonstrate their efficacy in-vivo using small animal models. The researchers of this paper, isolated and characterized the required monoclonal antibodies from recovering convalescent donors and developed neutralizing assays to investigate the antibody responses. In parallel, the researchers also developed both live attenuated and pseudovirus neutralization assays using HeLa- ACE2 (Angiotensin-converting enzyme) cell line. The collected convalescent plasma was evaluated against COVID-19 by using 8 donors. The antigen-specific B cells were sorted and corresponding genes were identified and cloned to enable antibody expression and characterization. The promising monoclonal antibodies were progressed for further testing in-vivo using a small animal model.

The study further isolated the potent neutralizing antibodies to two epitopes- the receptor-binding domain (RBD) and the non RBD- Spike (S) protein. The data showed that the passive transfer of neutralizing antibodies provides distinct protection against the novel- COVID-19 virus as seen in Syrian hamsters. The animal model throughout the infection maintained the same weight and showed low lung abnormalities. Nevertheless, as for any animal model, there were a few limitations, including the difference in receptor cells between the hamster and humans.

The results from the study suggest a focus on the RBD and a string neutralizing antibody responses were seen by immunizing mice with a multivalent RBD. The few weak preponderances of neutralizing antibody to S protein may be due to the result of the study using recombinant S protein. In conclusion, the data from the study potentially open up to the idea of the very rapid generation of neutralizing antibodies to a newly emerged novel virus. The antibodies can open up to the possibilities of finding a clinical application and will aid in vaccine manufacturing or design.

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