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  • First ‘gene silencing’ drug for Alzheimer’s disease shows promise

    First 'gene silencing' drug for Alzheimer's disease shows promise
    26th April 2023

    Effect of MAPTRx on CSF concentrations of p-tau protein and tau/Aβ42. a, The mean percentage change from baseline in p-tau over time according to dose group. b, The mean percentage change from baseline in the ratio of t-tau to Aβ42 over time according to dose group. Error bars indicate the standard error of the mean. Q4W and Q12W indicates dosing every 4 or 12 weeks, respectively. *Participants assigned to cohort A or B did not seamlessly transition to LTE part 2 and experienced a variable gap ranging from 5 to 19 months between completion of MAD part 1 at day 253 and start of LTE part 2 (D1P2). χPlacebo group was pooled. Subjects assigned to cohorts A or B and randomized to placebo had a variable gap between completion of MAD part 1 and start of LTE part 2 (D1P2). 

    A world-first trial at UCL and UCLH has found a new genetic therapy for Alzheimer’s disease that is able to safely and successfully lower levels of the harmful tau protein known to cause the disease. The trial, led by consultant neurologist Dr. Catherine Mummery (UCL Queen Square Institute of Neurology & the National Hospital for Neurology and Neurosurgery), represents the first time that a “gene silencing” approach has been taken in dementia and Alzheimer’s disease. The approach uses a drug called BIIB080 (/IONIS-MAPTRx), which is an antisense oligonucleotide (used to stop RNA producing a protein), to “silence” the gene coding for the tau protein-known as the microtubule-associated protein tau (MAPT) gene. This prevents the gene from being translated into the protein in a doseable and reversible way. It will also lower the production of that protein and alter the course of disease.

    By University College London

    Article can be accessed on: MedicalXpress

  • How do plant-soil-microbial interactions mediate vegetation dynamics?

    21st April 2023

    Graphical abstract. Credit: Science of The Total Environment (2023). DOI: 10.1016/j.scitotenv.2023.162393

    Studies have indicated that early and mid-successional plants generally suffered from negative to neutral plant-soil feedback. However, the role of such plant-soil feedback during the postglacial primary succession of plants and microorganisms has not been fully understood. In a study published in Science of The Total Environment, researchers sought to uncover the drivers of ecosystem succession during glacial retreat based on plant competition, microbial community structure and plant-soil-microbial interactions. They tested whether plant-soil-microbial interactions explain plant primary succession in the Gongga Mountain glacial retreat chronosequence.

    To determine how plant-soil-microbial interactions mediate the vegetation dynamics, the researchers treated soils from three stages of primary succession in the Mount Gongga glacial retreat with sterilized soil and living soil as controls, and then planted with combinations of intra and inter species competition.

    The researchers then used plant biomass to assess inter-plant competitiveness and the effects of habitat legacies. They then measured soil microbial community composition (bacteria and fungi) and mycorrhizal colonization rates to estimate the patterns and drivers of plant-soil-microbial interactions on aboveground plant succession.

    They found that plant-soil-microbial interactions were coincident with the occurrence of pioneer plants during primary succession in glacial retreat areas. The habitat legacy effect shifted from negative in the early stages to positive in the middle and late stages due to increasing nutrient availability and the presence of specific microbial groups.

    By Zhang Nannan, Chinese Academy of Sciences

    Article can be accessed on: phys.org

  • Lung cancer’s molecular features shed light on immunotherapy response

    Lung cancer's molecular features shed light on immunotherapy response
    20th April 2023

     

    Cancer cells (red), cell nuclei (cyan), stroma/desmoplasia (green), active stroma-specific marker (purple). Credit: National Cancer Institute 

    One of the newest types of cancer drugs, immunotherapies called immune checkpoint inhibitors, has transformed the treatment of lung cancer over the last decade dramatically improving the survival of some patients with the most common form of this disease, non-small cell lung cancer (NSCLC). However, only about 20% of patients experience a benefit from these immunotherapies.  A study led by researchers at the Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH) reveals molecular features of lung tumors that could explain why some patients respond to these treatments while others do not. The team has pinpointed several genetic and other biological factors that may influence the response of NSCLC patients to immunotherapies that inhibit the PD-1 or PD-L1 proteins.

    The research team examined whole-exome sequencing and RNA-sequencing data from tumor samples contributed by nearly 400 NSCLC patients before treatment, along with information about their clinical responses to anti-PD-(L)1 therapy. This is one of the largest multi-omic datasets from NSCLC patients who have been treated with these medicines, enabling the scientists to identify a suite of molecular features that are associated with improved treatment outcomes. Results demonstrate the complexity of the biological factors that determine immunotherapy response, and suggest why existing methods of predicting treatment outcomes in NSCLC patients, which look at only a small number of molecular features, aren’t always accurate. The researchers said their study points to the potential for improving these predictions or even developing more personalized approaches to treatment based on a patient’s molecular profile.

    By Karen Zusi-Tran, Broad Institute of MIT and Harvard

    Article can be accessed on: MedicalXpress

  • Single Cell Sequencing in a Nutshell

    Single Cell Sequencing in a Nutshell
    5th April 2023

    Single cell sequencing is a collection of methods that researchers use to isolate and analyze sequence information from individual cells. Named the Nature Method of the Year in 2013,1 single cell sequencing techniques allow researchers to understand more than ever before about cells’ inner workings. Many traditional sequencing methods cannot help researchers analyze material from individual or small numbers of cells rather, they sequence bulk cell populations where a large number of cells, with their contents of interest, are pooled prior to analysis. Studying cells in bulk masks information about the cell-to-cell variability that exists in a population, presenting instead the population’s average genome. In contrast, single cell sequencing allows DNA or RNA from individual cells to be amplified and sequenced, capturing each cell’s uniqueness.2 

    Scientists often use single cell sequencing to detect genetic variants by analyzing the genome, understand epigenetic variation by sequencing the methylome, or track gene expression differences by investigating the transcriptome of individual cells in a population. Through these studies, researchers can identify rare yet important cell subtypes within heterogenous cell populations.

    Focus On: Single Cell RNA Sequencing (scRNA-seq)

    scRNA-seq has emerged as an important technique for identifying differences in cells that otherwise appear homogeneous and understanding cellular responses on the molecular level. In 2009, two years after researchers performed RNA sequencing using next-generation techniques on bulk cell samples, scientists from the Wellcome/Cancer Research UK Gurdon Institute at the University of Cambridge executed the method on a single cell.3 From there, scientists developed many forms of scRNA-seq that increased the number of cells assayed at a time, decreased costs, and enhanced data reliability over time.4

    A General scRNA-seq Workflow

    While scientists have developed numerous scRNA-seq protocols, many follow the same basic steps and principles.5

    • Cell isolation
    • Extraction and amplification of genetic material
    • Sequencing library preparation
    • Next-generation sequencing (NGS)
    • Data analysis

    Isolating living single cells from a tissue of interest is the most important step in this process. 5 If a sample contains few cells, such as an early-stage embryo, researchers can manually capture individuals through micropipetting. An additional low-throughput method, laser capture microdissection, allows researchers to isolate single cells directly from tissue.6  Higher-throughput methods typically involve first mechanically and/or enzymatically dissociating cells from tissue. Then, cells can be sorted into microwells via flow-activated cell sorting (FACS) or microdroplets through microfluidic technologies. 6,7 Alternatively, particularly if cells are multi-nucleated, researchers may prefer to isolate single nuclei.8 

    Modified from iStock.com, STUDIOM1

    After isolation, cells must be lysed to release their mRNA, which is typically captured by poly(T)-primers that bind to 3’ poly-(A) mRNA tails.5 The mRNA is then reverse transcribed into cDNA. During this step, researchers often add nucleic acid adaptors, barcodes, or other molecular identifiers, depending on the needs of the sequencing method utilized, to the ends of the cDNA.5 The generated cDNA is at this point in minute quantities—the nucleic acid sequence is then amplified either by PCR or in vitro transcription.

    For NGS, scientists then prepare cDNAs according to the needs of the sequencing method, often necessitating barcoding, pooling, and quality control at this step. After sequencing is complete, researchers use bioinformatic and/or computational tools to assess data quality and analyze and interpret results. scRNA-seq data is noisy with many confounding factors that affect the read counts.7 These include technical variation, such as amplification bias and dropouts, and biological variation due to a cell’s environmental niche or where they were in the cell cycle during isolation. Bioinformatics methods such as principal component analysis help researchers cluster cell subpopulations based on differential gene expression patterns and define or refine molecular relationships between single cells.5

    Single Cell Sequencing in Action

    To date, researchers have used various types of single cell sequencing approaches in a range of life science fields. A few examples of single cell sequencing in action include analyzing genomic copy number variation, DNA methylation, and gene expression changes during human colorectal cancer metastasis,9 identifying different brain cell subtypes that are more susceptible to common risk factors for brain diseases,10 and determining molecular characteristics and key regulators of spermatogenesis.11 Additionally, more than 2,600 researchers worldwide have come together to use single cell and spatial techniques to create reference maps of all human cells through the Human Cell Atlas project.12,13

    Finally, as technology advances, scientists have combined different single cell sequencing methods with other techniques to better understand the connection between a cell’s genome and ultimate functions. With these multiomic approaches—combining genomics with transcriptomics, transcriptomics with epigenomics, or transcriptomics with proteomics, for example—researchers are gaining crucial insights that transform the understanding of health and disease.14

    By Niki Spahich, PhD

    Article can be accessed on: TheScientist

     

  • Fast and low-cost computational method can monitor spread of antibiotic resistance over time

    Fast and low-cost computational method can monitor spread of antibiotic resistance over time
    28th March 2023

    DNA is made up of adenine-thymine (AT) bonds and guanine-cytosine (GC) bonds. The frequency of each type of bond differs substantially across bacterial species. A new study uses this quirk of bacterial genetics to determine the origin and spread of various genes for antibiotic resistance. The new technique is so fast and inexpensive that it could be applied at regular intervals to track changes to bacterial genomes over time and detect emerging antibiotic resistance threats. Credit: National Human Genome Research Institute

    Growing resistance to antibiotics and other antimicrobial treatments is a serious global healthcare challenge. A new study in Antibiotics demonstrates a method for tracking the spread of genes for antimicrobial resistance among bacterial populations over time. The new computational technique relies on the rapidly increasing availability of bacterial genetic sequences in public databases such as GenBank.

    With the code Erill and colleagues Miquel Sánchez-Osuna and Jordi Barbé at the Universitat Autònoma de Barcelona developed, it’s possible to analyze the sequences of all known bacterial plasmids (little circular pieces of DNA that can exchange genes between bacteria) in about an hour. The results reveal which resistance genes are spreading most and the genes’ likely origin.

    A computational analysis like this is much faster and less expensive than complex systems involving coordination among clinicians around the world. This means it could be carried out more frequently to help doctors and researchers stay updated on shifting resistance threats.

    By Sarah Hansen, University of Maryland Baltimore County

    Article can be accessed on: Medicalxpress

  • Researchers report novel cell size regulation mechanism in cyanobacteria

    Researchers report novel cell size regulation mechanism in cyanobacteria
    28th March 2023

    Credit: IHB

    Cyanobacteria are the earliest known oxygenic photosynthetic organisms on Earth, and they played decisive roles in the evolution of the environment and the life on our planet. Cell morphology and cell size of different cyanobacteria species vary widely, but each species has inheritable and distinct cell morphology and cell size that are stably maintained through the generations. The underlying mechanisms of such a homeostasis have been unknown. In a study published in Proceedings of the National Academy of Sciences, the research group led by Prof. Zhang Chengcai from the Institute of Hydrobiology (IHB) of the Chinese Academy of Sciences reported a new cell size regulation mechanism which represents an important advance in understanding cell morphology and cell size controlling of cyanobacteria. Previous studies by Prof. Zhang’s group have showed that the second messenger c-di-GMP may be an intracellular proxy for cell size control in the filamentous cyanobacterium Anabaena PCC 7120. However, it was unclear how this chemical signal is perceived in cells. In addition, all c-di-GMP receptors known in other organisms are not conserved in cyanobacteria, posing a challenge to understand the physiological function of c-di-GMP in cyanobacteria.

    In this study, the researchers identified and characterized the first c-di-GMP receptor, CdgR, from the cyanobacterium Anabaena. Crystal structural analysis and genetic studies showed that CdgR binds c-di-GMP at the dimer interface, and this binding is required for the control of cell size in a c-di-GMP-dependent manner.

     

    By Liu Jia, Chinese Academy of Sciences

    Article can be accessed on: phys.org

  • Mutational Signature Indicates Risk of Kidney Cancer Recurrence

    Mutational Signature Indicates Risk of Kidney Cancer Recurrence
    3rd March 2023

    For decades, surgery was the only treatment option for patients with localized clear-cell renal cell carcinoma, a common type of kidney cancer. But the cancer tended to return in people with more aggressive tumors around one-third of patients often popping up in other organs. Clinicians now treat such cases with immunotherapy, a type of adjuvant therapy that helps fight tumors by bolstering the body’s immune system. But identifying patients at high risk of recurrence has proven difficult.

    Yasser Riazalhosseini, a geneticist at McGill University in Canada, would often hear his clinician colleagues complain about the lack of tools for assessing relapse in kidney cancer patients. They were forced to rely on traditional methods, such as assessing tumor grade and how aggressive the cancer appears under the microscope (histological analysis), even though these are known to be poor predictors of cancer recurrence. “This was our motivation for us to use genomics to develop biomarkers that can be used to stratify patients based on risk of relapse,” Riazalhosseini tells The Scientist.

    See “‘Silent’ Mutation Linked to Worse Kidney Cancer Outcomes

    In a paper published February 23 in Clinical Cancer Research, Riazalhosseini and colleagues sequenced the DNA of tumors removed from 943 people with clear-cell renal cell carcinoma (ccRCC). They searched for mutations in 12 genes associated with the condition, then tracked the cancer patients’ outcomes for the next five years.

    All patients harbored a mutation in a tumor suppressor gene called von Hippel-Lindau (VHL). Mutations in VHL are known to trigger kidney cancer, but cells may accumulate additional mutations in other genes linked to the condition. In this case, 91 percent of patients who only carried a mutation in VHL remained cancer-free five years after surgery. But this dropped to 80 percent in patients with one additional mutation in a ccRCC-linked gene, and just 51 percent in individuals with two extra mutations. The findings could be used to make informed decisions about which patients will benefit from immunotherapy, say the researchers.

    “At the moment, we’re in danger of over-treating patients,” says Naveen Vasudev, study coauthor and a medical oncologist at the University of Leeds in the United Kingdom. Immunotherapy is expensive and potentially toxic; avoiding unnecessary treatment can give low-risk patients a better quality of life, he adds.

    See “How Kidney Cancer Evolves

    Marston Linehan, Chief of Urologic Surgery at the National Cancer Institute in Maryland, who was not involved in the study says that the study’s “elegant findings” suggest that routine DNA sequencing of tumors following surgery could be useful for selecting kidney cancer patients for adjuvant therapy.

    Toni Choueiri, a medical oncologist at Harvard’s Dana-Farber Cancer Institute who did not take part in the research warns that “a higher risk of recurrence doesn’t necessarily mean that patients will benefit from adjuvant therapy.” Validating the results in a large study that tracks high-risk patients undergoing further therapy is an important next step, he adds.

    By Holly Barker, PhD

    Article can be accessed on: TheScientist

  • Cyclic RNA switches that regulate gene expression in a cell type-specific manner

    Cyclic RNA switches that regulate gene expression in a cell type-specific manner
    17th February 2023

    Credit: Nucleic Acids Research (2023). DOI: 10.1093/nar/gkac1252

    The Hirohide Saito Laboratory has developed cyclic RNA switches that can control gene expression in a cell type-specific manner using miRNAs and RNA-binding proteins and has successfully constructed an artificial gene circuit by combining them. Gene transfer technology using synthetic mRNA has the advantages of low risk of genome damage and high transfer efficiency compared to DNA, and thus has a wide range of potential applications, including vaccines, gene therapy, and genome editing. However, RNA is unstable in the cell, making it difficult to sustain gene expression, which is an application challenge.

    Because they are not easily degraded in the cells, cyclic RNAs are more stable than linear mRNAs and therefore attracting attention as a new synthetic mRNA that improves RNA persistence. However, specific introduction of mRNA into target cells has been difficult, and unintended protein expression in non-target cells may lead to reduced therapeutic efficacy and side effects in medical applications of mRNA. Therefore, it is necessary to develop a technology to control protein expression (gene expression) from cyclic RNA, but this has not been realized yet. The Saito laboratory started to work on the development of an RNA switch technology that can control gene expression of cyclic RNAs according to cell type. The research group synthesized cyclic RNAs that are intended to respond to endogenous miRNAs to regulate gene expression.

     by Kyoto University

    Article can be accessed on: phys.org

     

  • Increasing the visibility of African research and researchers

    Increasing the visibility of African research and researchers
    10th February 2023

    Credit: unsplash/cc0 public domain

    Consortium for Advanced Research Training in Africa (CARTA) launches Evidence website. CARTA was established in 2008 to support the development of a vibrant African academy able to lead world-class multidisciplinary research that impacts positively on public and population health. A collaboration jointly led by the Wits University in South Africa and the African Population and Health Research Center in Kenya, CARTA aims to rebuild and to strengthen the capacity of African universities to produce world-class researchers, research leaders, and scholars.

    To this end, CARTA Evidence is a new prototype website to collate, analyse, synthesise, and increase the visibility of empirical and theoretical evidence that these African networks produce, and to facilitate its uptake. The CARTA Evidence project aims to increase the visibility of PhD students and early career researchers by publishing the work they do. The interactive platform creates a collaborative environment of African scientists working across boundaries and disciplines and enables users to search for research conducted by CARTA fellows and other public and population health researchers across sub-Saharan Africa.  Notably, CARTA Evidence technology can review over a million research papers in a few hours or days, depending on the type of the request. The application of such artificial intelligence (AI) mitigates human error in reviewing papers manually, and reduces the time involved.

    “I am excited by the prospects of using Artificial intelligence (AI) to improve the efficiency of research capacity building, conducting research, generating new knowledge and innovation to address public health challenges in Africa,” says Jude Igumbor, Associate Professor in the School of Public Health who, along with his team in the Wits School of Public Health and the CARTA Secretariat collaborated to bring CARTA Evidence to fruition.

    CARTA Evidence hosts five main features:

    • Bibliometric Dashboard – designed to demonstrate the impact and content of research done by CARTA fellows. The page has filters and search boxes to show trends and patterns between and within groups over time.
    • Publications feature, which presents a list of publications by CARTA fellows, making it easy to review research. The tab uses keywords and filters, which enable effective searches for documents, access articles on various subjects and a description of data and design used in the studies.
    • Abstract Summaries page facilitates rapid research evidence review by generating auto-summaries of research articles
    • Impact Search-Africa curates public and population health research articles to improve access to evidence and data in Africa. The page allows for searches for high impact articles, data, and scientists in sub-Saharan Africa, by number of citations.
    • Policy briefs  page has a growing list of short articles and policy briefs on priority public health issues in Africa, including an article on making the most of doctoral students in COVID-19 response.

    By The University of the Witwatersrand, South Africa, Johannesburg

     

  • AI technology generates original proteins from scratch

    AI technology generates original proteins from scratch
    27th January 2023

    Scientists have created an AI system capable of generating artificial enzymes from scratch. In laboratory tests, some of these enzymes worked as well as those found in nature, even when their artificially generated amino acid sequences diverged significantly from any known natural protein. The experiment demonstrates that natural language processing, although it was developed to read and write language text, can learn at least some of the underlying principles of biology. Salesforce Research developed the AI program, called ProGen, which uses next-token prediction to assemble amino acid sequences into artificial proteins. Scientists said the new technology could become more powerful than directed evolution, the Nobel-prize winning protein design technology, and it will energize the 50-year-old field of protein engineering by speeding the development of new proteins that can be used for almost anything from therapeutics to degrading plastic. “The artificial designs perform much better than designs that were inspired by the evolutionary process,” said James Fraser, Ph.D., professor of bioengineering and therapeutic sciences at the UCSF School of Pharmacy, and an author of the work, which was published Jan. 26, in Nature Biotechnology. A previous version of the paper has been available on the preprint server BiorXiv since July of 2021, where it garnered several dozen citations before being published in a peer-reviewed journal.

    by University of California, San Francisco

    Article can be accessed on: phys.org