Hasil untuk "Biology (General)"

J. Arlett, E. Myers, M. Roukes

C. Edwards, J. Lofty

A. Onuki

Jing Liu, S. Chakraborty, P. Hosseinzadeh et al.

Redox reactions play important roles in almost all biological processes, including photosynthesis and respiration, which are two essential energy processes that sustain all life on earth. It is thus not surprising that biology employs redox-active metal ions in these processes. It is largely the redox activity that makes metal ions uniquely qualified as biological cofactors and makes bioinorganic enzymology both fun to explore and challenging to study. Even though most metal ions are redox active, biology employs a surprisingly limited number of them for electron transfer (ET) processes. Prominent members of redox centers involved in ET processes include cytochromes, iron–sulfur clusters, and cupredoxins. Together these centers cover the whole range of reduction potentials in biology (Figure ​(Figure1).1). Because of their importance, general reviews about redox centers1−77 and specific reviews about cytochromes,8,24,78−90 iron–sulfur proteins,91−93 and cupredoxins94−104 have appeared in the literature. In this review, we provide both classification and description of each member of the above redox centers, including both native and designed proteins, as well as those proteins that contain a combination of these redox centers. Through this review, we examine structural features responsible for their redox properties, including knowledge gained from recent progress in fine-tuning the redox centers. Computational studies such as DFT calculations become more and more important in understanding the structure–function relationship and facilitating the fine-tuning of the ET properties and reduction potentials of metallocofactors in proteins. Since this aspect has been reviewed extensively before,105−110 and by other reviews in this thematic issue,2000,2001,2002 it will not be covered here. Figure 1 Reduction potential range of redox centers in electron transfer processes.

K. Berland, V. Cooper, Kyuho Lee et al.

A density functional theory (DFT) that accounts for van der Waals (vdW) interactions in condensed matter, materials physics, chemistry, and biology is reviewed. The insights that led to the construction of the Rutgers–Chalmers van der Waals density functional (vdW-DF) are presented with the aim of giving a historical perspective, while also emphasizing more recent efforts which have sought to improve its accuracy. In addition to technical details, we discuss a range of recent applications that illustrate the necessity of including dispersion interactions in DFT. This review highlights the value of the vdW-DF method as a general-purpose method, not only for dispersion bound systems, but also in densely packed systems where these types of interactions are traditionally thought to be negligible.

Wei Wang, M. A. Knovich, L. Coffman et al.

L. Bernatchez, C. Landry

Martin Wu, Ling V. Sun, J. Vamathevan et al.

The complete sequence of the 1,267,782 bp genome of Wolbachia pipientis wMel, an obligate intracellular bacteria of Drosophila melanogaster, has been determined. Wolbachia, which are found in a variety of invertebrate species, are of great interest due to their diverse interactions with different hosts, which range from many forms of reproductive parasitism to mutualistic symbioses. Analysis of the wMel genome, in particular phylogenomic comparisons with other intracellular bacteria, has revealed many insights into the biology and evolution of wMel and Wolbachia in general. For example, the wMel genome is unique among sequenced obligate intracellular species in both being highly streamlined and containing very high levels of repetitive DNA and mobile DNA elements. This observation, coupled with multiple evolutionary reconstructions, suggests that natural selection is somewhat inefficient in wMel, most likely owing to the occurrence of repeated population bottlenecks. Genome analysis predicts many metabolic differences with the closely related Rickettsia species, including the presence of intact glycolysis and purine synthesis, which may compensate for an inability to obtain ATP directly from its host, as Rickettsia can. Other discoveries include the apparent inability of wMel to synthesize lipopolysaccharide and the presence of the most genes encoding proteins with ankyrin repeat domains of any prokaryotic genome yet sequenced. Despite the ability of wMel to infect the germline of its host, we find no evidence for either recent lateral gene transfer between wMel and D. melanogaster or older transfers between Wolbachia and any host. Evolutionary analysis further supports the hypothesis that mitochondria share a common ancestor with the α-Proteobacteria, but shows little support for the grouping of mitochondria with species in the order Rickettsiales. With the availability of the complete genomes of both species and excellent genetic tools for the host, the wMel–D. melanogaster symbiosis is now an ideal system for studying the biology and evolution of Wolbachia infections.

E. Cupp

Samuel Stevens, Jiaman Wu, Matthew J. Thompson et al.

Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. There is an ex-plosion of computational methods and tools, particularly computer vision, for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions, contexts, and datasets. A vision model for general or-ganismal biology questions on images is of timely need. To approach this, we curate and release Tree Of Life-10m, the largest and most diverse ML-ready dataset of biology images. We then develop Bioclip, a foundation model for the tree of life, leveraging the unique properties of bi-ology captured by Treeoflife-10m, namely the abun-dance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge. We rigorously benchmark our approach on di-verse fine-grained biology classification tasks and find that BloCLIP consistently and substantially outperforms existing baselines (by 16% to 17% absolute). Intrinsic evaluation reveals that BloCLIP has learned a hierarchical representation conforming to the tree of life, shedding light on its strong generalizability.11imageomics.github.io/bioclip has models, data and code.

Domitilla Del Vecchio

This paper gives an overview of the use of control systems engineering in synthetic biology, motivated by applications such as cell therapy and cell fate reprogramming for regenerative medicine. A ubiquitous problem in these and other applications is the ability to control the concentration of specific regulatory factors in the cell accurately despite environmental uncertainty and perturbations. The paper describes the origin of these perturbations and how they affect the dynamics of the biomolecular ``plant'' to be controlled. A variety of biomolecular control implementations are then introduced to achieve robustness of the plant's output to perturbations and are grouped into feedback and feedforward control architectures. Although sophisticated control laws can be implemented in a computer today, they cannot be necessarily implemented inside the cell via biomolecular processes. This fact constraints the set of feasible control laws to those realizable through biomolecular processes that can be engineered with synthetic biology. After reviewing biomolecular feedback and feedforward control implementations, mostly focusing on the author's own work, the paper illustrates the application of such control strategies to cell fate reprogramming. Within this context, a master regulatory factor needs to be controlled at a specific level inside the cell in order to reprogram skin cells to pluripotent stem cells. The article closes by highlighting on-going challenges and directions of future research for biomolecular control design.

Dipesh Singh Chuphal, Vimal Mishra

Abstract Reliable streamflow projections are essential for effective water‐resource management and climate adaptation. However, streamflow projections are associated with large uncertainties due to divergent precipitation projections from climate models, which directly propagate into hydrological estimates. Observation‐constrained approaches that condition future projections on past observations have been shown to reduce such uncertainties; however, they have not been applied to streamflow projections across the Indian rivers. Using long‐term streamflow and global mean surface temperature observations, climate model projections, hydrological modeling, and a Bayesian detection–attribution framework, we developed observational constrained streamflow projections for nine major Indian rivers. The method reduces the 5–95% confidence interval of future streamflow projections by nearly one‐third compared to raw multimodel ensembles, with constraint strength controlled by internal streamflow variability and inter‐model spread in the unconstrained ensemble. Projection uncertainty is further reduced to ∼20% when considering projections based only on skillful climate models. Constrained projections indicate significant increases in streamflow in the near‐, mid‐, and far‐future periods, except for the Cauvery basin, which shows a near‐term decline. Applying the method to raw precipitation projections reveals comparable constraint strength and increases confidence in the results, given the strong dependence of Indian river flows on precipitation. Our findings underscore the importance of combining skillful climate models with post‐processing constraint methods to substantially reduce model‐based uncertainty. Overall, our results provide critical insights into future streamflow changes across Indian rivers, supporting long‐term water‐resource planning and climate‐resilient management.

Società Italiana di Biologia Sperimentale

Nature-based compounds are increasingly investigated as eco-friendly tools to mitigate pollutant-induced toxicity in aquatic organisms, due to their richness in bioactive molecules (e.g., polyphenols, flavonoids, and other antioxidants) able to support cellular stress, immune competence, organisms’ resilience and homeostasis1. Beyond their biological potential, these approaches also fit a circular bioeconomy perspective, as they enable the valorisation of olive oil industry residues. Indeed, converting these low-value side streams into high-value extracts may reduce waste and promote sustainable innovation. Within this framework, the present investigation aimed to obtain novel insights into the potential protective and mitigatory role of the olive leaf extract (OLE), obtained from olive oil industry residual biomass, on the cellular and physiological performances of Mytilus galloprovincialis exposed to the neonicotinoid insecticide thiacloprid (THI), which is already recognised as a contaminant capable of compromising the health of organisms2. Bioactive molecules were obtained by solvent extraction of olive-derived residues, and the extract was then subjected to characterisation. Specimens were exposed to THI (4.5 μg/L), OLE (5 mg/L), and their mixture (THI+OLE), for fourteen days. Endpoints addressed immune competence, e.g., haemocyte functional activity and cytoskeleton-related signalling; cellular functionality and vitality, e.g., lysosomal stability and membrane integrity in haemocytes and digestive gland (DG) cells; redox balance through selected molecular biomarkers linked to antioxidant defences and cellular protection; cell osmoregulatory capacity through the evaluation the ability of DG cells to cope with osmotic challenge and restore cell volume. Results showed a consistent pattern across all analyses. No significant differences were observed between the control and OLE groups, whereas significant changes emerged in the THI-treated groups. Notably, co-exposure (THI+OLE) was associated with a recovery toward control-like levels. Overall, these findings support and provide novel insights into the potential of olive leaf-derived bioactive compounds to mitigate contaminant-induced toxicity, while highlighting the bioeconomic value of upgrading olive oil processing residues into functional extracts for environmentally sustainable applications in aquatic toxicology. In a broader “One Health” perspective, improving the resilience of marine bivalves contributes not only to ecosystem integrity but also to human well-being, considering the high commercial value of Mytilus galloprovincialis as a widely farmed and consumed seafood species and its central role in coastal economies and food supply chains.

A. Oshlack, M. Wakefield

BackgroundSeveral recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data.ResultsWe investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript.ConclusionTranscript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses.ReviewersThis article was reviewed by Rohan Williams (nominated by Gavin Huttley), Nicole Cloonan (nominated by Mark Ragan) and James Bullard (nominated by Sandrine Dudoit).

U. Wilensky, K. Reisman

Yanke Li, Tianyu Cui, Tommaso Mansi et al.

Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human genome remains infeasible due to the vast search space of potential genetic interactions and experimental constraints. Bayesian optimization (BO) has emerged as a powerful framework for selecting informative interventions, but existing approaches often fail to exploit domain-specific biological prior knowledge. We propose Biology-Informed Bayesian Optimization (BioBO), a method that integrates Bayesian optimization with multimodal gene embeddings and enrichment analysis, a widely used tool for gene prioritization in biology, to enhance surrogate modeling and acquisition strategies. BioBO combines biologically grounded priors with acquisition functions in a principled framework, which biases the search toward promising genes while maintaining the ability to explore uncertain regions. Through experiments on established public benchmarks and datasets, we demonstrate that BioBO improves labeling efficiency by 25-40%, and consistently outperforms conventional BO by identifying top-performing perturbations more effectively. Moreover, by incorporating enrichment analysis, BioBO yields pathway-level explanations for selected perturbations, offering mechanistic interpretability that links designs to biologically coherent regulatory circuits.

Claire Delehouzé, Melodie Mallais, Arnaud Comte et al.

Abstract In the past two decades, various non-apoptotic pathways of regulated cell death have been identified; a small subset of these, including necroptosis and ferroptosis, manifests the phenotypic features of necrotic death. These two regulated necroses are being extensively studied because of their putative roles in severe acute and chronic pathologies. Moreover, as these regulated necrotic pathways are coactivated in a number of common pathologies, the development of multi-target directed ligands (that is, the use of a polypharmacological strategy) is a path-breaking avenue of research. In this study, we determined that the 7-azaindole derivative, sibiriline, inhibited both RIPK1-driven necroptosis (induced by Tumor Necrosis Factor-α) and ferroptosis (triggered by various classes of ferroptosis inducers), with EC50s against each in the µM range. We next performed a combined large-scale transcriptomic study in order to determine the molecular mechanisms of action of sibiriline. We identified the stress response protein heme oxygenase-1 (HMOX1) as the main biomarker of ferroptosis inhibition by sibiriline. We hypothesized that this compound reacts as an antioxidant to block ferroptosis; indeed, we found that sibiriline inhibits lipid peroxidation by trapping phospholipid-derived peroxyl radicals as a radical-trapping antioxidant (RTA). Taken together, these results show that sibiriline is a new dual inhibitor of necroptosis and ferroptosis cell death pathways; it works by inhibition of both RIPK1 kinase and (phospho)lipid peroxidation. We also demonstrate the in vitro efficacy of sibiriline to inhibit cell death in cell-based models of Parkinson’s disease and cystic fibrosis. These findings shed light on the high therapeutic potency of RIPK1 inhibitors with RTA activity.

Ruifeng Xiao, Cong Shen, Wen Shen et al.

Fyn is widely involved in diverse cellular physiological processes, including cell growth and survival, and has been implicated in the regulation of energy metabolism and the pathogenesis of diabetes mellitus through multiple pathways. Fyn plays a role in increasing fat accumulation and promoting insulin resistance, and it also contributes to the development of diabetic complications such as diabetic kidney disease and diabetic retinopathy. The primary mechanism by which Fyn modulates lipid metabolism is that it inhibits AMP-activated protein kinase (AMPK). Additionally, it affects energy homeostasis through regulating specific signal pathways affecting lipid metabolism including pathways related to CD36, through enhancement of adipocyte differentiation, and through modulating insulin signal transduction. Inflammatory stress is one of the fundamental mechanisms in diabetes mellitus and its complications. Fyn also plays a role in inflammatory stress-related signaling cascades such as the Akt/GSK-3β/Fyn/Nrf2 pathway, exacerbating inflammation in diabetes mellitus. Therefore, Fyn emerges as a promising therapeutic target for regulating glucolipid metabolism and alleviating type 2 diabetes mellitus. This review synthesizes research on the role of Fyn in the regulation of energy metabolism and the development of diabetes mellitus, while exploring its specific regulatory mechanisms.

O. Chis, J. Banga, E. Balsa-Canto

Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be used to estimate unknown parameters. However, in many cases, a subset of those parameters may not be uniquely estimated, independently of the experimental data available or the numerical techniques used for estimation. This lack of identifiability is related to the structure of the model, i.e. the system dynamics plus the observation function. Despite the interest in knowing a priori whether there is any chance of uniquely estimating all model unknown parameters, the structural identifiability analysis for general non-linear dynamic models is still an open question. There is no method amenable to every model, thus at some point we have to face the selection of one of the possibilities. This work presents a critical comparison of the currently available techniques. To this end, we perform the structural identifiability analysis of a collection of biological models. The results reveal that the generating series approach, in combination with identifiability tableaus, offers the most advantageous compromise among range of applicability, computational complexity and information provided.