Nanopore sequencing enables high-resolution analysis

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.

A critical review on quantum dots

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.

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.”

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

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.