Current opinion in biotechnology最新文献

Messenger RNA (mRNA) vaccines have made remarkable public health contributions during the pandemic and initiated a new era for nucleic acid–based therapeutics. With the unique strength of nucleic acids, including not only mRNA but also DNA, microRNA, small interfering RNA (siRNA), and other nucleic acids, either in tuning off genes or introducing function, nucleic acid therapeutics have been regarded as potential candidates for the treatment of many different diseases, especially for the immunomodulation in cancer. However, the scope of the applications was limited by the challenges in delivery due to intrinsic properties of nucleic acids including low stability, immunogenicity, and toxicity. Bioengineering approaches toward efficient and targeted delivery of therapeutic nucleic acids have gained momentum in clinical applications in the past few decades. Recent advances in the biotechnological approaches for the delivery of mRNA, siRNA, and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas for immunomodulatory are promising alternatives in designing future cancer immunotherapy.

Despite success in treating hematologic malignancies, chimeric antigen receptor-T cell (CAR-T) therapy still faces multiple challenges that have halted progress, especially against solid tumors. Recent advances in nanoscale engineeirng provide several avenues for overcoming these challenges, including more efficienct programming of CAR-Ts ex vivo, promoting immune responsiveness in the tumor microenvironment (TME) in vivo, and boosting CAR-T function in situ. Here, we summarize recent innovations that leverage nanotechnology to directly address the major obstacles that impede CAR-T therapy from reaching its full potential across various cancer types. We conclude with a commentary on the state of the field and how nanotechnology can shape the future of CAR-T and adoptive cell therapy in immuno-oncology.

Phosphorus (P) enrichment of water impairs its quality by stimulating algal growth and eutrophication, affecting an estimated 1.7 billion people. Remediation costs are substantial, estimated at $1 billion annually in Europe and $2.4 billion in the USA. Agricultural intensification over the past 50 years has increased P use brought into the system from mined fertiliser sources. This has enriched soil P concentrations and loss to surface waters via pathways such as surface runoff and subsurface flow, which are influenced by precipitation, slope, and farming practices. Effective mitigation of losses involves managing P sources, mobilisation, and transport/delivery mechanisms. The cost-effectiveness of mitigation actions can be improved if they are targeted to critical source areas (CSAs), which are small zones that disproportionately contribute to P loss. While targeting CSAs works well in areas with variable topography, flatter landscapes require managing legacy sources, such as enriched soil P to prevent P losses.

Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. The cell size detected by single-cell omics ranges from ∼2 µm (e.g., Bacillus subtilis) to ∼120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules in situ. The recent advances in Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging and Micro/Nanodroplet Processing in One Pot for Trace Samples for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.

In recent years, the rapid advancement of generative artificial intelligence (GenAI) has revolutionized the landscape of drug design, offering innovative solutions to potentially expedite the discovery of novel therapeutics. GenAI encompasses algorithms and models that autonomously create new data, including text, images, and molecules, often mirroring characteristics of existing datasets. This comprehensive review delves into the realm of GenAI for drug design, emphasizing recent advancements and methodologies that have propelled the field forward. Specifically, we focus on three prominent paradigms: transformers, diffusion models, and reinforcement learning algorithms, which have been exceptionally impactful in the last few years. By synthesizing insights from a myriad of studies and developments, we elucidate the potential of these approaches in accelerating the drug discovery process. Through a detailed analysis, we explore the current state and future directions of GenAI in the context of drug design, highlighting its transformative impact on pharmaceutical research and development.

The advent of highly efficient genome editing (GE) tools, coupled with high-throughput genome sequencing, has paved the way for the accelerated domestication of crop wild relatives. New crops could thus be rapidly created that are well adapted to cope with drought, flooding, soil salinity, or insect damage. De novo domestication avoids the complexity of transferring polygenic stress resistance from wild species to crops. Instead, new crops can be created by manipulating major genes in stress-resistant wild species. However, the genetic basis of certain relevant domestication-related traits often involve epistasis and pleiotropy. Furthermore, pan-genome analyses show that structural variation driving gene expression changes has been selected during domestication. A growing body of work suggests that the Solanaceae family, which includes crop species such as tomatoes, potatoes, eggplants, peppers, and tobacco, is a suitable model group to dissect these phenomena and operate changes in wild relatives to improve agronomic traits rapidly with GE. We briefly discuss the prospects of this exciting novel field in the interface between fundamental and applied plant biology and its potential impact in the coming years.

Lignin valorization faces persistent biomanufacturing challenges due to the heterogeneous and toxic carbon substrates derived from lignin depolymerization. To address the heterogeneous nature of aromatic feedstocks, plant cell wall engineering and ‘lignin first’ pretreatment methods have recently emerged. Next, to convert the resulting aromatic substrates into value-added chemicals, diverse microbial host systems also continue to be developed. This includes microbes that (1) lack aromatic metabolism, (2) metabolize aromatics but not sugars, and (3) co-metabolize both aromatics and sugars, each system presenting unique pros and cons. Considering the intrinsic complexity of lignin-derived substrate mixtures, emerging and non-model microbes with native metabolism for aromatics appear poised to provide the greatest impacts on lignin valorization via biomanufacturing.

G protein–coupled receptors (GPCRs) are the largest family of transmembrane receptors in humans. Over 800 GPCRs regulate the (patho)biology of every organ, tissue, and cell type. Consequently, GPCRs are the most prominent therapeutic targets in medicine. Although over 30% of current U.S. Food and Drug Administration-approved drugs target GPCR signaling, most receptors remain understudied and therapeutically underutilized. Challenges include an incomplete understanding of GPCR signaling, pharmacology, structural biology, and the multiplicity of endogenous GPCR ligands, in addition to a scarcity of biological and pharmacological tools for elucidating GPCR-mediated cellular processes beyond initial signaling events. Various mammalian, insect, and yeast cell models currently address some of these needs. Here, we review recent advances in yeast synthetic biology that are helping to catalyze new and unexpected conceptual and technical breakthroughs in GPCR-based medicine and biotechnology.

Emerging biotechnologies that solve pressing environmental and climate emergencies will require harnessing the vast functional diversity of the underlying microbiomes driving such engineered processes. Modeling is a critical aspect of process engineering that informs system design as well as aids diagnostic optimization of performance. ‘Conventional’ bioprocess models assume homogenous biomass within functional guilds and thus fail to predict emergent properties of diverse microbial physiologies, such as product specificity and community interactions. Yet, recent advances in functional ‘omics-based approaches can provide a ‘lens’ through which we can probe and measure in situ ecophysiologies of environmental microbiomes. Here, we overview microbial community modeling approaches that incorporate functional ‘omics data, which we posit can advance our ability to design and control new environmental biotechnologies going forward.

Bioelectrochemical sensor (BES) technologies have been developed to measure soluble carbon concentrations in wastewater. However, architectures and analytical methods developed in controlled laboratory environments fail to predict BES behavior during field deployments at water resource recovery facilities (WRRFs). Here, we examine the possibilities and obstacles associated with integrating BESs into environmental sensing networks and machine learning algorithms to monitor the biodegradable carbon dynamics and microbial metabolism at WRRFs. This approach highlights the potential of BESs to provide real-time insights into full-scale biodegradable carbon consumption across WRRFs.