Hasil untuk "Biology (General)"

D. Sperber, D. Premack, A. Premack

RenHui Wu, MengMeng Yang, TianChi Wu et al.

Background In recent years, skin health has garnered widespread attention, with hyperpigmentation disorders caused by excessive melanin deposition emerging as a particularly prominent concern. Tyrosinase, as the rate-limiting enzyme in the melanin synthesis process, has long been a major focus in the development of its inhibitors. However, only a limited number of tyrosinase inhibitors are currently available for clinical treatment of such disorders, and they are associated with certain toxicity concerns. Consequently, there is an urgent need to develop novel inhibitors that combine high efficacy with low toxicity. Recent studies have shown that deep learning technology exhibits strong capabilities in uncovering the intrinsic patterns of data and predicting the biological activities of compounds, providing a significant opportunity for the rapid screening of novel tyrosinase inhibitors. Methods Based on a dataset of tyrosinase-related compounds, this study constructed a deep learning model to predict compounds that inhibit tyrosinase activity. Using this model, we conducted activity predictions for 36,585 compound and selected the top 100 molecules with the highest prediction scores for screening and verification. Results Literature comparison revealed that 53 of these molecules had been reported to inhibit tyrosinase activity, providing initial support for the model’s reliability. After further screening based on specific criteria, 10 candidate molecules were ultimately selected for molecular docking studies. The docking results indicated that these molecules had good binding potential with the target protein, indirectly supporting the accuracy of the model’s prediction. The final experimental verification revealed that compounds 5 and 10 significantly inhibited tyrosinase activity and reduced melanin content.

Rayyah R. Alkhanjari, Maitha M. Alhajeri, Poorna Manasa Bhamidimarri et al.

Abstract Septins are GTP-binding cytoskeletal proteins primarily known to be involved in cell division, membrane remodeling, and cytoskeletal organization. In the nervous system, septins are suggested as key regulators of neural development, including neurite outgrowth, spine morphology, and axon initial segment formation. Septins are localized to specialized membrane domains, such as dendritic spines, axon initial segments, and synaptic terminals, where they function as scaffolding components and diffusion barriers. They are abundant in neurons, oligodendrocytes, Schwann cells, and astrocytes, regulating processes like myelination and synaptic organization. In neuronal cells, specific septin isoforms such as SEPT3, SEPT5, and SEPT7 contribute to dendritic spine formation, neurotransmitter vesicle trafficking, and axonal integrity. Alterations in septin expression or assembly can disrupt synaptic architecture and neuroplasticity, emphasizing their role in neuronal homeostasis. Dysregulation of septin expression and function has been implicated in a range of neurological disorders, including demyelinating diseases like Multiple Sclerosis and Hereditary Neuralgic Amyotrophy. Abnormal septin aggregation has been observed in neurodegenerative diseases such as Alzheimer's and Parkinson's disease. Moreover, septins can modulate inflammatory responses, where antibodies for septins 5 and 7 were associated with autoimmune encephalitis conditions. This review will provide a comprehensive overview of the role of septins in the nervous system, focusing on their molecular mechanisms, cellular functions, and implications in neurological disorders.

Mohammad Souri, Sohail Elahi, Farshad Moradi Kashkooli et al.

Abstract Intratumoral delivery and localized chemotherapy have demonstrated promise in tumor treatment; however, the rapid drainage of therapeutic agents from well-vascularized tumors limits their ability to achieve maximum therapeutic efficacy. Therefore, innovative approaches are needed to enhance treatment efficacy in such tumors. This study utilizes a mathematical modeling platform to assess the efficacy of combination therapy using anti-angiogenic drugs and drug-loaded nanoparticles. Anti-angiogenic drugs are included to reduce blood microvascular density and facilitate drug retention in the extracellular space. In addition, incorporating negatively charged nanoparticles aims to enhance diffusion and distribution of therapeutic agents within well-vascularized tumors. The findings indicate that, in the case of direct injection of free drugs, using compounds with lower drainage rates and higher diffusion coefficients is beneficial for achieving broader diffusion. Otherwise, drugs tend to accumulate primarily around the injection site. For instance, the drug doxorubicin, known for its rapid drainage, requires the prior direct injection of an anti-angiogenic drug with a high diffusion rate to reduce microvascular density and facilitate broader distribution, enhancing penetration depth by 200%. Moreover, the results demonstrate that negatively charged nanoparticles effectively disperse throughout the tissue due to their high diffusion coefficient. In addition, a faster drug release rate from nanoparticles further enhance treatment efficacy, achieving the necessary concentration for complete eradication of tumor compared to slower drug release rates. This study demonstrates the potential of utilizing negatively charged nanoparticles loaded with chemotherapy drugs exhibiting high release rates for localized chemotherapy through intratumoral injection in well-vascularized tumors.

Nils Wetzstein, Margo Diricks, Thomas B. Anton et al.

Abstract Background The Mycobacterium avium complex (MAC) comprises the most frequent non-tuberculous mycobacteria (NTM) in Central Europe and currently includes twelve species. M. avium (MAV), M. intracellulare subsp. intracellulare (MINT), and M. intracellulare subsp. chimaera (MCH) are clinically most relevant. However, the population structure and genomic landscape of MAC linked with potential pathobiological differences remain little investigated. Methods Whole genome sequencing (WGS) was performed on a multi-national set of MAC isolates from Germany, France, and Switzerland. Phylogenetic analysis was conducted, as well as plasmids, resistance, and virulence genes predicted from WGS data. Data was set into a global context with publicly available sequences. Finally, detailed clinical characteristics were associated with genomic data in a subset of the cohort. Results Overall, 610 isolates from 465 patients were included. The majority could be assigned to MAV (n = 386), MCH (n = 111), and MINT (n = 77). We demonstrate clustering with less than 12 SNPs distance of isolates obtained from different patients in all major MAC species and the identification of trans-European or even trans-continental clusters when set into relation with 1307 public sequences. However, none of our MCH isolates clustered closely with the heater-cooler unit outbreak strain Zuerich-1. Known plasmids were detected in MAV (325/1076, 30.2%), MINT (62/327, 19.0%), and almost all MCH-isolates (457/463, 98.7%). Predicted resistance to aminoglycosides or macrolides was rare. Overall, there was no direct link between phylogenomic grouping and clinical manifestations, but MCH and MINT were rarely found in patients with extra-pulmonary disease (OR 0.12 95% CI 0.04–0.28, p < 0.001 and OR 0.11 95% CI 0.02–0.4, p = 0.004, respectively) and MCH was negatively associated with fulfillment of the ATS criteria when isolated from respiratory samples (OR 0.28 95% CI 0.09-0.7, p = 0.011). With 14 out of 43 patients with available serial isolates, co-infections or co-colonizations with different strains or even species of the MAC were frequent (32.6%). Conclusions This study demonstrates clustering and the presence of plasmids in a large proportion of MAC isolates in Europe and in a global context. Future studies need to urgently define potential ways of transmission of MAC isolates and the potential involvement of plasmids in virulence.

David Ellerman

This is an essay in what might be called ``mathematical metaphysics.'' There is a fundamental duality that run through mathematics and the natural sciences. The duality starts as the logical level; it is represented by the Boolean logic of subsets and the logic of partitions since subsets and partitions are category-theoretic dual concepts. In more basic terms, it starts with the duality between the elements (Its) of subsets and the distinctions (Dits, i.e., ordered pairs of elements in different blocks) of a partition. Mathematically, the Its $\&$ Dits duality is fully developed in category theory as the reverse-the-arrows duality. The quantitative versions of subsets and partitions are developed as probability theory and information theory (based on logical entropy). Classical physics was based on a view of reality as definite all the way down. In contrast, quantum physics embodies (objective) indefiniteness. And finally, there are the two fundamental dual mechanisms at work in biology, the selectionist mechanism and the generative mechanism, two mechanisms that embody the fundamental duality.

Susobhan Mandal, S. Shankaranarayanan

General relativity and quantum field theory are the cornerstones of our understanding of physical processes, from subatomic to cosmic scales. While both theories work remarkably well in their tested domains, they show minimal overlap. However, our research challenges this separation by revealing that non-perturbative effects bridge these distinct domains. We introduce a novel mechanism wherein, at linear order, spin-2 fields around an arbitrary background acquire \emph{effective mass} due to the spontaneous symmetry breaking (SSB) of either global or local symmetry of complex scalar field minimally coupled to gravity. The action of the spin-2 field is identical to the extended Fierz-Pauli (FP) action, corresponding to the mass deformation parameter $α= 1/2$. We show that this occurs due to the effect of SSB on the variation of the energy-momentum tensor of the matter field, which has a dominant effect during SSB. The extended FP action has a salient feature, compared to the standard FP action: the action has 6 degrees of freedom with no ghosts. For local $U(1)$ SSB, we establish that the effective mass of spin-2 fields is related to the mass of the gauge boson and the electric charge of the complex scalar field. Interestingly, our results indicate that the millicharged dark matter scalar fields, generating dark photons, can produce a mass of spin-2 fields of the same order as the Hubble constant $(H_0)$. Hence, we argue that the dark sector offers a natural explanation for the acceleration of the current Universe.

Ievgeniia A. Tiukova, Daniel Brunnsåker, Erik Y. Bjurström et al.

The cutting edge of applying AI to science is the closed-loop automation of scientific research: robot scientists. We have previously developed two robot scientists: `Adam' (for yeast functional biology), and `Eve' (for early-stage drug design)). We are now developing a next generation robot scientist Genesis. With Genesis we aim to demonstrate that an area of science can be investigated using robot scientists unambiguously faster, and at lower cost, than with human scientists. Here we report progress on the Genesis project. Genesis is designed to automatically improve system biology models with thousands of interacting causal components. When complete Genesis will be able to initiate and execute in parallel one thousand hypothesis-led closed-loop cycles of experiment per-day. Here we describe the core Genesis hardware: the one thousand computer-controlled $μ$-bioreactors. For the integrated Mass Spectrometry platform we have developed AutonoMS, a system to automatically run, process, and analyse high-throughput experiments. We have also developed Genesis-DB, a database system designed to enable software agents access to large quantities of structured domain information. We have developed RIMBO (Revisions for Improvements of Models in Biology Ontology) to describe the planned hundreds of thousands of changes to the models. We have demonstrated the utility of this infrastructure by developed two relational learning bioinformatic projects. Finally, we describe LGEM+ a relational learning system for the automated abductive improvement of genome-scale metabolic models.

A. Martell, R. D. Hancock

M. Alser, Jeremy Rotman, Dhrithi Deshpande et al.

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today’s diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

L. D. Quin

Diogo de Mayrinck, Alexandre Cunha Ribeiro, Mario Luis Assine et al.

The Mesozoic Teleostei †Ichthyodectiformes presents a widespread distribution in marine brackish and freshwater deposits worldwide. The Brazilian fossil record of this group is represented by five nominal genera distributed exclusively in the sedimentary basins of Northeast Brazil (cf. Parnaíba, Sergipe-Alagoas, Recôncavo, Tucano, and Araripe). In the Araripe basin, the unique representative of the order is †<i>Cladocyclus gardneri</i>, restricted to the Crato and Romualdo Formations. Recent collecting efforts carried out in the Araripe Basin led to the discovery of two specimens of †Cladocyclidae. Based on the comparison with the known Brazilian taxa, we conclude that this new record represents a new genus and species of this clade. †<i>Cladocynodon araripensis</i> represents the first vertebrate described from the dark shales of the “Batateira Beds” of the Barbalha Formation and differs from the other †Cladocyclidae by the presence of hypertrophied bony fangs at the anterior region of the dentary, with other relatively small true teeth abruptly reduced posteriorly, and by presenting premaxillary and maxillary teeth significantly reduced in size. †<i>Cladocynodon araripensis</i> increases the anatomic diversity and temporal range of †Cladocyclidae in Gondwana.

Davinia Vicente-Campos, Sandra Sánchez-Jorge, Luis Martí et al.

Oxidative stress has been proposed as a significant part of the pathogenesis of fibromyalgia, and the phase angle in bioelectrical impedance analysis has been explored as a potential technique to screen oxidative abnormalities. This study recruited 35 women with fibromyalgia and 35 healthy women, who underwent bioelectrical impedance analysis and maximum isometric handgrip strength tests. Women with fibromyalgia showed lower bilateral handgrip strength (right hand: 16.39 ± 5.87 vs. 27.53 ± 4.09, <i>p</i> < 0.001; left hand: 16.31 ± 5.51 vs. 27.61 ± 4.14, <i>p</i> < 0.001), as well as higher body fat mass (27.14 ± 10.21 vs. 19.94 ± 7.25, <i>p</i> = 0.002), body fat percentage (37.80 ± 8.32 vs. 30.63 ± 7.77, <i>p</i> < 0.001), and visceral fat area (136.76 ± 55.31 vs. 91.65 ± 42.04, <i>p</i> < 0.01) compared with healthy women. There was no statistically significant difference in muscle mass between groups, but women with fibromyalgia showed lower phase angles in all body regions when compared with healthy control women (right arm: 4.42 ± 0.51 vs. 4.97 ± 0.48, <i>p</i> < 0.01; left arm: 4.23 ± 0.48 vs. 4.78 ± 0.50, <i>p</i> < 0.001; trunk: 5.62 ± 0.77 vs. 6.78 ± 0.84, <i>p</i> < 0.001; right leg: 5.28 ± 0.56 vs. 5.81 ± 0.60, <i>p</i> < 0.001; left leg: 5.07 ± 0.51 vs. 5.69 ± 0.58, <i>p</i> < 0.001; whole body: 4.81 ± 0.47 vs. 5.39 ± 0.49, <i>p</i> < 0.001). Moreover, whole-body phase-angle reduction was only predicted by the presence of fibromyalgia (<i>R</i>² = 0.264; β = 0.639; F_(1,68) = 24.411; <i>p</i> < 0.001). Our study revealed significantly lower phase angle values, lower handgrip strength, and higher fat levels in women with fibromyalgia compared to healthy controls, which are data of clinical relevance when dealing with such patients.

Mallana Gowdra Mallikarjuna, Manish Kumar Pandey, Rinku Sharma et al.

Ian Dunn, David Ryan Koes

Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design. These models operate directly on 3D molecular structures. Due to the unfavorable scaling of graph neural networks (GNNs) with graph size as well as the relatively slow inference speeds inherent to diffusion models, many existing molecular diffusion models rely on coarse-grained representations of protein structure to make training and inference feasible. However, such coarse-grained representations discard essential information for modeling molecular interactions and impair the quality of generated structures. In this work, we present a novel GNN-based architecture for learning latent representations of molecular structure. When trained end-to-end with a diffusion model for de novo ligand design, our model achieves comparable performance to one with an all-atom protein representation while exhibiting a 3-fold reduction in inference time.

Einar Bjarki Gunnarsson, Jasmine Foo, Kevin Leder

Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.

E. Wolkovich, E. Cleland

L. Erdős, Didem Ambarlı, O. Anenkhonov et al.

1Institute of Ecology and Botany, MTA Centre for Ecological Research, Vácrátót, Hungary 2Faculty of Agriculture and Natural Sciences, Düzce University, Konuralp, Turkey 3Institute of General and Experimental Biology SB RAS, Ulan-Ude, Russia 4Department of Ecology, University of Szeged, Szeged, Hungary 5Department of Botany, University of Veterinary Medicine, Budapest, Hungary 6Department of Climatology and Landscape Ecology, University of Szeged, Szeged, Hungary 7College of Urban and Environmental Sciences, Peking University, Beijing, China 8Institute of Plant Sciences, University of Graz, Graz, Austria 9Department of Biology, Faculty of Basic Sciences, University of Mazandaran, Mazandaran, Iran 10Department of Biology, I. G. Petrovsky Bryansk State University, Bryansk, Russia 11MTA-DE Lendület Functional and Restoration Ecology Research Group, Debrecen, Hungary

C. Nelson, Kate E Ihle, M. Fondrk et al.

Temporal division of labor and foraging specialization are key characteristics of honeybee social organization. Worker honeybees (Apis mellifera) initiate foraging for food around their third week of life and often specialize in collecting pollen or nectar before they die. Variation in these fundamental social traits correlates with variation in worker reproductive physiology. However, the genetic and hormonal mechanisms that mediate the control of social organization are not understood and remain a central question in social insect biology. Here we demonstrate that a yolk precursor gene, vitellogenin, affects a complex suite of social traits. Vitellogenin is a major reproductive protein in insects in general and a proposed endocrine factor in honeybees. We show by use of RNA interference (RNAi) that vitellogenin gene activity paces onset of foraging behavior, primes bees for specialized foraging tasks, and influences worker longevity. These findings support the view that the worker specializations that characterize hymenopteran sociality evolved through co-option of reproductive regulatory pathways. Further, they demonstrate for the first time how coordinated control of multiple social life-history traits can originate via the pleiotropic effects of a single gene that affects multiple physiological processes.

M. Volgraf, P. Gorostiza, R. Numano et al.