Emerging Topics in Life Sciences最新文献

The onset of the Fourth Industrial Revolution has catalysed a fundamental shift in how research within the molecular life sciences is approached and undertaken. Over the past decade, a multitude of nascent enabling technologies have progressed to maturity and have become irreversibly embedded in laboratory practice. Artificial intelligence (AI) has become a mainstay within the molecular sciences, facilitating major advances across a multitude of sub-disciplines, including synthetic biology, industrial biotechnology and drug discovery. One area where this impact is being particularly felt is within multi-omics, where the marriage of AI with low-cost high-throughput sequencing is delivering unprecedented advances, allowing large and often complex datasets to be deconvoluted on timescales previously considered unimaginable. In this mini-review, we outline how the integration of AI into multi-omics has been realised and forecast future trajectories for research in this important area.

Insect farming is widely extolled as a sustainable alternative to the traditional agricultural production of protein for human and animal consumption. However, pathogen contamination endangers insect health, food safety, production yields and market acceptance. Because insect farming is intensive, growth and transmission of pathogens are promoted, elevating the risk of disease outbreaks with severe economic outcomes. Fungal pathogens can invade host insects through their cuticle, reproducing within the nutrientrich haemolymph within the haemocoel until the host's defences are overwhelmed and the insect dies. Other pathogens, such as viruses, oomycetes and bacteria, enter the host orally before penetrating the midgut wall to infect the haemolymph. Even apparently healthy farmed insects carry a diverse array of potentially pathogenic bacteria/fungi within their guts, as well as sub-lethal viral infections, and these covert infections can quickly become epizootic breakouts. Consequently, there is an urgent need to understand the infection and transmission of pathogens in insect farms, as well as to develop strategies to prevent and treat infections/outbreaks. This review collates information about the susceptibility of farmed insects to infection by fungi, bacteria, viruses, nematodes and other parasites, current pathogen detection methods, and possible control measures, with the aim of making this information accessible to practitioners and researchers of insect farming. We suggest that prophylactics/treatments are urgently needed by insect farms, alongside improvements in infection control, to ensure the long-term viability and acceptance of edible insects as a sustainable alternative protein source.

Artificial intelligence (AI) technologies have the potential to revolutionise traditional drug discovery and development. The applications of AI in four prominent areas of drug discovery-drug design, quantum computing, precision medicine and biomarker discovery-are all covered in this issue of Emerging Topics in Life Sciences and summarised in this editorial.

Microorganisms are the primary source of genetic diversity on earth due to their unparalleled metabolic and functional variability. With the depletion of fossil fuels, a sustainable alternative approach is the use of biofuels, where plant biomass as feedstock is essentially degraded to sugars with the aid of microbe-derived enzymes, followed by the conversion of those sugars to biofuels. Several cellulolytic and non-cellulolytic enzymes are involved in biofuel synthesis. Molecular cloning, along with the advancements in genetic and metabolic engineering in microbial cells, plays a significant contribution to biofuel overproduction. Advanced molecular technologies such as metagenomics and synthetic biology approaches are also being used to construct effective microorganisms for biofuel manufacturing. Obtaining novel enzymes from undiscovered microbial consortia and functional gene analysis is possible through a metagenomics approach. While synthetic biology provides engineered biological systems to generate required biofuel productivity, the CRISPR-Cas genome editing tool is another revolutionary tool being utilized for efficient biofuel production. This article provides a brief overview of different methods of biofuel production using microorganisms.

Efficient human waste management and hygiene maintenance are vital for long-duration space missions. By using bioelectrochemical systems, specifically microbial fuel cells (MFCs) combined with hydroponics, human waste can potentially be converted into a valuable commodity. Recent advancements in MFCs indicate a significant potential for generating electricity (1-2 mW/single MFC/ml of urine) and biofertilisers concurrently from urine and sewage while suppressing human pathogens that may be present. Integrating MFCs with hydroponics opens up the possibility to balance nutrients in human waste while growing vegetables in MFC-powered hydroponic systems, using only a small percentage of synthetic fertilisers, if deemed necessary. This is a concise perspective of the potential of MFCs for nutrient recycling from human waste and vegetable production that could enhance the self-sustainability of a spacecraft or mission.

Microalgae are a promising feedstock for biodiesel due to their rapid growth, high lipid content and ability to use non-arable land and wastewater. This review synthesises recent advances in artificial intelligence (AI)-driven strain optimisation, engineering, nanotechnology-assisted processing, and life cycle and technoeconomic insights to evaluate pathways for industrialisation. Over the past decade (2015-2024), genetic engineering and, more recently, AI-guided strain selection have improved lipid productivity by up to 40%. Cultivation advances, including hybrid photobioreactor-open pond systems and precision pH/CO2 control, have enhanced biomass yields while reducing costs. Innovation in lipid extraction, such as supercritical CO2 and microwave-assisted methods, now achieves >90% yields with lower toxicity, while magnetic nanoparticle-assisted harvesting and electroflocculation have reduced energy inputs by 20-30%. Life cycle analyses (net energy ratio ~2.5) and integration of high-value co-products (e.g. pigments and proteins) underscore the need to align biological innovations with techno-economic feasibility. This review uniquely integrates advances in AI, CRISPR and nanotechnology with life cycle and techno-economic perspectives, providing a comprehensive framework that links laboratory-scale innovation to industrial feasibility and positions microalgal biodiesel as a viable contributor to global decarbonisation strategies.

Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure. Here, we provide an overview of the AI toolkit available for biomarker discovery, and we discuss exciting examples of AI-powered biomarkers across therapeutic areas. Finally, we address the challenges ahead of us to ensure that these technologies reach patients and users globally and unlock a new era of fast innovation for precision medicine.

Precision medicine marks a transformative shift towards a patient-centric treatment approach, aiming to match 'the right patients with the right drugs at the right time'. The exponential growth of data from diverse omics modalities, electronic health records, and medical imaging has created unprecedented opportunities for precision medicine. This explosion of data requires advanced processing and analytical tools. At the forefront of this revolution is artificial intelligence (AI), which excels at uncovering hidden patterns within these high-dimensional and complex datasets. AI facilitates the integration and analysis of diverse data types, unlocking unparalleled potential to characterise complex diseases, improve prognosis, and predict treatment response. Despite the enormous potential of AI, challenges related to interpretability, reliability, generalisability, and ethical considerations emerge when translating these tools from research settings into clinical practice.

In the past decade, cryo-electron microscopy and single particle analysis (SPA) have quickly become key methods in structural biology. In particular, increased access to equipment and streamlined software has enabled new users to successfully carry out SPA projects. At the same time, cryo-electron tomography (cryo-ET) has also made great technical strides, most notably with cellular cryo-ET. While many challenges remain, developments in hardware and automation have made cellular cryo-ET specimen preparation and data collection more accessible than ever. There is also a growing field of cryo-ET software developers, but the wide variety of biological specimens and scientific goals that can be pursued using cryo-ET makes it difficult to develop processing workflows analogous to those in SPA; this becomes a major barrier to entry for new users. In this perspective, I make a case that the development of standardized metadata can play a key role in reducing such barriers and allow for an ecosystem that enables new users to enter the field while retaining a diversity of processing approaches.

Bacteria employ diverse mechanisms to manage toxic copper in their environments, and these evolutionary strategies can be divided into two main categories: accumulation and rationalization of metabolic pathways. The strategies employed depend on the bacteria's lifestyle and environmental context, optimizing the metabolic cost-benefit ratio. Environmental and opportunistically pathogenic bacteria often possess an extensive range of copper regulation systems in order to respond to variations in copper concentrations and environmental conditions, investing in diversity and/or redundancy as a safeguard against uncertainty. In contrast, obligate symbiotic bacteria, such as Neisseria gonorrhoeae and Bordetella pertussis, tend to have specialized and more parsimonious copper regulation systems designed to function in the relatively stable host environment. These evolutionary strategies maintain copper homeostasis even in challenging conditions like encounters within phagocytic cells. These examples highlight the adaptability of bacterial copper management systems, tailored to their specific lifestyles and environmental requirements, in the context of an evolutionary the trade-off between benefits and energy costs.