Bacterial Defences Investigations win ERC Starting Grant


can be seen in various forms including phages (viruses that infect bacteria) and plasmids (circular DNA molecules).


Dr Stineke Van Houte, at the University’s Environment and sustainability Institute has been awarded a €1.5 million European Research Council (ERC) Starting Grant for the project, called MUSIC (MGE Uptake and Spread in Microbial Communities), with a further €1 million for a Fluorescence- activated cell sorting (FACS) machine.


“This MUSIC project aims to understand how bacterial immune systems keep MGEs out,” said Dr Van Houte, “It will investigate the relative importance of the different individual bacterial defences, using a combination of bacterial genome sequence analyses and laboratory experiments.


Stineke van Houte and research team


To further our understanding of the movement of DNA between bacteria, researchers at the University of Exeter are undertaking a major project to investigate how the spread of these mobile genetic elements (MGEs) are influenced by bacterial defences.


MGEs, which can change key traits of bacteria such as antibiotic resistance and virulence (the severity of illness they can cause),


“The goal is to develop a comprehensive picture of which bacterial defences are particularly important in blocking MGE infections, and this information will allow us to predict whether a bacterium can or can’t become infected with an MGE, based solely on the defence repertoire found in that bacterium’s genome.


“From this, we will use machine learning approaches in order to build towards understanding how MGEs spread through communities of multiple bacterial species, based on the


knowledge of what defences exist in that community.”


The project’s ‘blue sky’ approach was not specifically focussed on human health – but on understanding key processes of bacterial evolution, he added. However, this understanding could be vital in the emerging antibiotic resistance crisis.


The award to Dr Van Houte, one of 397 ERC Starting Grants given to early-career researchers, from a total of €619 million awarded, are intended to help “ambitious younger researchers launch their own projects, form their teams and pursue their best ideas”.


ERC president Professor Maria Leptin said: “Letting young talent thrive in Europe and go after their most innovative ideas – this is the best investment in our future, not least with the ever-growing competition globally.


“We must trust the young and their insights into what areas will be important tomorrow. So, I am thrilled to see these new ERC Starting Grant winners ready to cut new ground and set up their own teams.”


More information online: ilmt.co/PL/woDa 57017pr@reply-direct.com


Intricate Structural Information Revealed by Deep Learning


The EMBL-European Bioinformatics Institute has been able to expand its open access protein family database (Pfam), with the help of deep learning models. Pfam provides insights for biologists on protein characteristics including vital protein annotations, structures and multiple sequence alignments and is widely used to classify protein sequences into phylogenies and identify domains that provide insights into protein activity.


The increase in knowledge content [1] was achieved through the use of deep learning methods developed by Google Research that were trained to use data from the Pfam research base to annotate previously undescribed protein domains, shedding light on potential protein function.


“Initially I was rather sceptical about using deep learning to reproduce the protein families within Pfam. Then I started collaborating more closely with Lucy Colwell and her team at Google Research and my scepticism quickly changed to excitement for the potential of these methods to improve our ability to classify sequences into domains and families,” said Alex Bateman, Senior Team Leader of Protein Sequence Resources at EMBL-EBI.


“These models exceed my expectations. They’re not just copying the data already in Pfam, they’re able to learn


from the data and find new information that is yet to be discovered. What this gives us is the ability to expand the Pfam collection and potentially that of other resources using these same deep learning methods.”


The project resulted in the expansion of the Pfam, database by almost 10%, exceeding previous expansion efforts made over the last decade. The deep learning methods were also able to predict the function for 360 human proteins that had no previous annotation data available in Pfam.


Using additional protein family predictions generated from the Google Research team’s neural networks created a supplement to Pfam called Pfam-N, (network) which added a further 6.8 million protein sequences to the Pfam database.


“We’re also now building on these established deep learning methods to expand the information in the database even further,” said Bateman. “We’re changing the way the existing deep learning model works so that we can call multiple protein domains at once. This new update to the database should be ready very soon.”


“My personal view is that there’s still a lot of scope to improve the deep learning models we’re currently using,” Bateman added. “We’re in the early days of this and I’m very hopeful for what it will mean for the future classification of


EMBL-EBI at the Wellcome Genome Campus, Hinxton, Cambridge. (Credit: EMBL-EBI)


protein families. This may even be something that will get solved in the next five years.”


This work is being funded by the Wellcome Trust. 1. Nature Biotechnology 21 Feb 2022 More information online: ilmt.co/PL/XZa6


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Step-change in Malaria Vector Identification has Impact Potential for other Diseases


With the infrared light providing information on the chemical composition of individual insect’s cuticles, the chemical changes of ageing mosquitos were identified using an AI algorithm. The scientists validated their predictions on wild mosquitoes with current methods, achieving similar results.


Doreen Siria, lead author from IHI, said: “Only mosquitos that live long enough to develop malaria – around ten days – can transmit the disease, so knowing the age of a mosquito can help inform the risk of disease.


“Previous identification methods via complex dissection of female mosquitos’ ovaries, was an expensive, time-consuming process that couldn’t be done at scale.”


Identifying aging mosquitos (Credit: University of Glasgow)


In a study to quickly identify aging mosquitos that are capable of transmitting the deadly malaria parasite, an international team led by scientists at the University of Glasgow’s Institute of Biodiversity Animal Health and Comparative Medicine (IBAHCM) and School of Chemistry, the Ifakara Health Instititute (IHI) in Tanzania and the Institut de Recherche en Sciences de la Santé (IRSS) in Burkina Faso, turned to the use of infrared spectroscopy and artificial intelligence (AI).


“This AI-driven infrared light technology requires a spectrometer currently costing around $20,000, which can be used as part of existing, routine malaria vector surveillance and offers a way to quickly establish if current intervention measures to reduce mosquito numbers in the wild are working, something which isn’t currently possible,” commented Roger Sanou, lead author from IRSS.


Dr Francesco Baldini, from the IBAHCM, said: “With this infrared technology, we have developed a tool which could be adopted within current mosquito control plans; has the potential to be scaled up for use across different areas; and would greatly help in testing new products and solutions against diseases transmitted by mosquitoes.


“We envision this approach could also be applied to other vectors and vector-borne diseases, from filariasis and chikungunya, to sleeping sickness and Zika; and could be used to evaluate the attempts to limit the expansion of invasive mosquito species across Europe and the United States.”


The resulting computer models can be adapted and implemented in the field for vector surveillance.


Simon Babayan, from the IBAHCM, said: “As these technologies become more accessible, we will move towards instantaneous data collection and analysis directly within, and potentially by, the communities that need to act on such information the most.”


Mario Gonzalez-Jimenez, from the School of Chemistry, added: “This work has shown that the same algorithms that allow us to recognise faces and objects in a photo are also able to identify the ways in which chemical compounds show their presence in a spectrum, even in samples as complex as a living being. We are witnessing how the use of AI is making possible chemical analyses that were unimaginable just a few years ago.”


The study was published in Nature Communications. More information online: ilmt.co/PL/ZkXP


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