Molecular Machinery Research Articles

A data-driven estimation of the ribosome drop-off rate in S. cerevisiae reveals a correlation with the genes length.

Ribosomes are the molecular machinery that catalyse all the fundamental steps involved in the translation of mRNAs into proteins. Given the complexity of this process, the efficiency of protein synthesis depends on a large number of factors among which ribosome drop-off (i.e. the premature detachment of the ribosome from the mRNA template) plays an important role. However, an in vitro quantification of the extent to which ribosome drop-off occurs is not trivial due to difficulties in obtaining the needed experimental evidence. In this work we focus on the study of ribosome drop-off in Saccharomyces cerevisiae by using 'Ribofilio', a novel software tool that relies on a high sensitive strategy to estimate the ribosome drop-off rate from ribosome profiling data. Our results show that ribosome drop-off events occur at a significant rate also when S. cerevisiae is cultured in standard conditions. In this context, we also identified a correlation between the ribosome drop-off rate and the genes length: the longer the gene, the lower the drop-off rate.

A strategy to build a library of oil tracers by oleophilic Silica-encapsulated DNA nanoparticles.

Artificially synthesized DNA is involved in the construction of a library of oil tracers due to their unlimited number and no-biological toxicity. The strategy of the construction is proposed by oleophilic Silica-encapsulated DNA nanoparticles, which offers fresh thinking in developing novel tracers, sensors, and molecular machines in engineering & applied sciences based on artificially synthesized DNA blocks.&#xD.

The amyloid precursor protein and its derived fragments concomitantly contribute to the alterations of mitochondrial transport machinery in Alzheimer’s disease

Mitochondria dysfunctions and mitophagy failure have been associated with several Alzheimer’s disease (AD) related molecular actors including amyloid beta (Aβ) and recently the amyloid precursor protein-C terminal fragments (APP-CTFs). The efficacy of the mitophagy process in neurons relies on regulated mitochondrial transport along axons involving a complex molecular machinery. The contribution of the amyloid precursor protein (APP) and its derived fragments to the mitochondrial transport machinery alterations in AD have not been investigated before. We report herein a change of the expression of mitochondrial transport proteins (SNPH and Miro1), motor adapters (TRANK1 and TRAK2), and components of the dynein and kinesin motors (i.e., IC1,2 and Kif5 (A, B, C) isoforms) by endogenous APP and by overexpression of APP carrying the familial Swedish mutation (APPswe). We show that APP-CTFs and Aβ concomitantly regulate the expression of a set of transport proteins as demonstrated in APPswe cells treated with β- and γ-secretase inhibitors and in cells Knock-down for presenilin 1 and 2. We further report the impact of APP-CTFs on the expression of transport proteins in AAV-injected C99 mice brains. Our data also indicate that both Aβ oligomers (Aβo) and APP-CTFs impair the colocalization of mitochondria and transport proteins. This has been demonstrated in differentiated SH-SY5Y naive cells treated with Aβo and in differentiated SH-SY5Y and murine primary neurons expressing APPswe and treated with the γ-secretase inhibitor. Importantly, we uncover that the expression of a set of transport proteins is modulated in a disease-dependent manner in 3xTgAD mice and in human sporadic AD brains. This study highlights molecular mechanisms underlying mitochondrial transport defects in AD that likely contribute to mitophagy failure and disease progression.

Exploring mitochondrial heteroplasmy in neonates: implications for growth patterns and overweight in the first years of life.

Mitochondrial heteroplasmy reflects genetic diversity within individuals due to the presence of varying mitochondrial DNA (mtDNA) sequences, possibly affecting mitochondrial function and energy production in cells. Rapid growth during early childhood is a critical development with long-term implications for health and well-being. In this study, we investigated if cord blood mtDNA heteroplasmy is associated with rapid growth at 6 and 12 months and overweight in childhood at 4-6 years. This study included 200 mother-child pairs of the ENVIRONAGE birth cohort. Whole mitochondrial genome sequencing was performed to determine mtDNA heteroplasmy levels (in variant allele frequency; VAF) in cord blood. Rapid growth was defined for each child as the difference between WHO-SD scores of predicted weight at either 6 or 12 months and birth weight. Logistic regression models were used to determine the association of mitochondrial heteroplasmy with rapid growth and childhood overweight. Determinants of relevant cord blood mitochondrial heteroplasmies were identified using multiple linear regression models. One % increase in VAF of cord blood MT-D-Loop16362T > C heteroplasmy was associated with rapid growth at 6 months (OR = 1.03; 95% CI: 1.01-1.05; p = 0.001) and 12 months (OR = 1.02; 95% CI: 1.00-1.03; p = 0.02). Furthermore, this variant was associated with childhood overweight at 4-6 years (OR = 1.01; 95% CI 1.00-1.02; p = 0.05). Additionally, rapid growth at 6 months (OR = 3.00; 95% CI: 1.49-6.14; p = 0.002) and 12 months (OR = 4.05; 95% CI: 2.06-8.49; p < 0.001) was also associated with childhood overweight at 4-6 years. Furthermore, we identified maternal age, pre-pregnancy BMI, maternal education, parity, and gestational age as determinants of cord blood MT-D-Loop16362T > C heteroplasmy. Our findings, based on mitochondrial DNA genotyping, offer insights into the molecular machinery leading to rapid growth in early life, potentially explaining a working mechanism of the development toward childhood overweight.

Selenoureido Calix[6]arenes: A Novel Platform for Pseudorotaxane Synthesis

Heteroditopic calix[6]arenes are widely employed macrocycles for the synthesis of pseudo‐oriented interlocked systems and stimuli‐induced molecular machines, among others. Herein, we introduce a new calix[6]arene receptor functionalised with three phenylselenoureido groups. These hydrogen‐bonding donor moieties are able to promote the threading of viologen‐based organic axles inside the macrocyclic cavity to form a stable pseudorotaxane species. Preliminary investigations using 1H‐NMR measurements and semi‐empirical and DFT studies suggested its further use as a platform for the synthesis of rotaxanes.

Protocol for the establishment of a serine integrase-based platform for functional validation of genetic switch controllers in eukaryotic cells.

Serine integrases (Ints) are a family of site-specific recombinases (SSRs) encoded by some bacteriophages to integrate their genetic material into the genome of a host. Their ability to rearrange DNA sequences in different ways including inversion, excision, or insertion with no help from endogenous molecular machinery, confers important biotechnological value as genetic editing tools with high host plasticity. Despite advances in their use in prokaryotic cells, only a few Ints are currently used as gene editors in eukaryotes, partly due to the functional loss and cytotoxicity presented by some candidates in more complex organisms. To help expand the number of Ints available for the assembly of more complex multifunctional circuits in eukaryotic cells, this protocol describes a platform for the assembly and functional screening of serine-integrase-based genetic switches designed to control gene expression by directional inversions of DNA sequence orientation. The system consists of two sets of plasmids, an effector module and a reporter module, both sets assembled with regulatory components (as promoter and terminator regions) appropriate for expression in mammals, including humans, and plants. The complete method involves plasmid design, DNA delivery, testing and both molecular and phenotypical assessment of results. This platform presents a suitable workflow for the identification and functional validation of new tools for the genetic regulation and reprogramming of organisms with importance in different fields, from medical applications to crop enhancement, as shown by the initial results obtained. This protocol can be completed in 4 weeks for mammalian cells or up to 8 weeks for plant cells, considering cell culture or plant growth time.

Charge govern ion-selective mechanism of YX2 desalination pore using high-throughput computations and machine learning

Energy-efficient and low-cost desalination of water continues to be a problem that society is facing. With the advances in nanotechnology, a variety of nanopore membranes have emerged for desalination. In particular, YX2 nanopores (YW, Mo, etc. and X = S, Se, Te, etc.) have been considered as promising membranes for water purification. In this work, we used a combination of high-throughput molecular dynamics simulations and machine learning to reveal the nanopore mass transfer mechanism and rationally design the YX2 nanopore. The pore size and the charge of the pore mouth were found to be the main factors affecting water permeation and salt rejection, as discovered through the machine learning study. The physical-chemical analysis provides us with a deep understanding of the charge-governed mechanism on the desalination performance of YX2 nanopores. It revealed that YX2 nanopores with a conical shape and minimized pore charges could be ideal candidates for excellent desalination performance. Based on this, we designed the YX2 pore with a specific charge distribution, which could achieve high water permeation with high salt rejection. Therefore, the combination of machine learning and high-throughput computation methods could aid in designing materials with excellent performance for desalination.

A Testis-Specific DMRT1 (Double Sex and Mab-3-Related Transcription Factor 1) Plays a Role in Spermatogenesis and Gonadal Development in the Hermaphrodite Boring Giant Clam Tridacna crocea

The testis-specific double sex and mab-3-related transcription factor 1 (DMRT1) has long been recognized as a crucial player in sex determination across vertebrates, and its essential role in gonadal development and the regulation of spermatogenesis is well established. Here, we report the cloning of the key spermatogenesis-related DMRT1 cDNA, named Tc-DMRT1, from the gonads of Tridacna crocea (T. crocea), with a molecular weight of 41.93 kDa and an isoelectric point of 7.83 (pI). Our hypothesis is that DMRT1 machinery governs spermatogenesis and regulates gonadogenesis. RNAi-mediated Tc-DMRT1 knockdown revealed its critical role in hindering spermatogenesis and reducing expression levels in boring giant clams. A histological analysis showed structural changes, with normal sperm cell counts in the control group (ds-EGFP) but significantly lower concentrations of sperm cells in the experimental group (ds-DMRT1). DMRT1 transcripts during embryogenesis exhibited a significantly high expression pattern (p &lt; 0.05) during the early zygote stage, and whole-embryo in-situ hybridization confirmed its expression pattern throughout embryogenesis. A qRT-PCR analysis of various reproductive stages revealed an abundant expression of Tc-DMRT1 in the gonads during the male reproductive stage. In-situ hybridization showed tissue-specific expression of DMRT1, with a positive signal detected in male-stage gonadal tissues comprising sperm cells, while no signal was detected in other stages. Our study findings provide an initial understanding of the DMRT1 molecular machinery controlling spermatogenesis and its specificity in male-stage gonads of the key bivalve species, Tridacna crocea, and suggest that DMRT1 predominantly functions as a key regulator of spermatogenesis in giant clams.

Insights into Energy Transfer in Light-Harvesting Complex II Through Machine-Learning Assisted Simulations.

Light-harvesting complex II (LHCII) is the major antenna of higher plants. Energy transfer processes taking place inside its aggregate of chlorophylls have been experimentally investigated with time-resolved techniques, but a complete understanding of the most relevant energy transfer pathways and relative characteristic times remains elusive. Theoretical models to disentangle experimental data in LHCII have long been challenged by the large size and complex nature of the system. Here, we show that a fully first-principles approach combining molecular dynamics and machine learning can be successfully used to reproduce transient absorption spectra and characterize the EET pathways and the involved times.

Cellular mechanisms of ion and acid-base regulation in teleost gill ionocytes.

The mechanism(s) of sodium, chloride and pH regulation in teleost fishes has been the subject of intense interest for researchers over the past 100 years. The primary organ responsible for ionoregulatory homeostasis is the gill, and more specifically, gill ionocytes. Building on the theoretical and experimental research of the past, recent advances in molecular and cellular techniques in the past two decades have allowed for substantial advances in our understanding of mechanisms involved. With an increased diversity of teleost species and environmental conditions being investigated, it has become apparent that there are multiple strategies and mechanisms employed to achieve ion and acid-base homeostasis. This review will cover the historical developments in our understanding of the teleost fish gill, highlight some of the recent advances and conflicting information in our understanding of ionocyte function, and serve to identify areas that require further investigation to improve our understanding of complex cellular and molecular machineries involved in iono- and acid-base regulation.

Sex-Dimorphic Effects of Hypoglycemia on Metabolic Sensor mRNA Expression in Ventromedial Hypothalamic Nucleus-Dorsomedial Division (VMNdm) Growth Hormone-Releasing Hormone Neurons.

Growth hormone-releasing hormone (Ghrh) neurons in the dorsomedial ventromedial hypothalamic nucleus (VMNdm) express the metabolic transcription factor steroidogenic factor-1 and hypoglycemia-sensitive neurochemicals of diverse chemical structures, transmission modes, and temporal signaling profiles. Ghrh imposes neuromodulatory control of coexpressed transmitters. Multiple metabolic sensory mechanisms are employed in the brain, including screening of the critical nutrient glucose or the energy currency ATP. Here, combinatory laser-catapult-microdissection/single-cell multiplex qPCR tools were used to investigate whether these neurons possess molecular machinery for monitoring cellular metabolic status and if these biomarkers exhibit sex-specific sensitivity to insulin-induced hypoglycemia. Data show that hypoglycemia up- (male) or downregulated (female) Ghrh neuron glucokinase (Gck) mRNA; Ghrh gene silencing decreased baseline and hypoglycemic patterns of Gck gene expression in each sex. Ghrh neuron glucokinase regulatory protein (Gckr) transcript levels were respectively diminished or augmented in hypoglycemic male vs female rats; this mRNA profile was decreased by Ghrh siRNA in both sexes. Gene transcripts encoding catalytic alpha subunits of the energy monitor 5-AMP-activated protein kinase (AMPK), i.e., Prkaa1 and 2, were increased by hypoglycemia in males, yet only the former mRNA was hypoglycemia-sensitive in females. Ghrh siRNA downregulated baseline and hypoglycemia-associated Prkaa subunit mRNAs in males but elicited divergent changes in Prkaa2 transcripts in eu- vs hypoglycemic females. Results provide unique evidence that VMNdm Ghrh neurons express the characterized metabolic sensor biomarkers glucokinase and AMPK and that the corresponding gene profiles exhibit distinctive sex-dimorphic transcriptional responses to hypoglycemia. Data further document Ghrh neuromodulation of baseline and hypoglycemic transcription patterns of these metabolic gene profiles.

Predicting Drug Resistance in Gastric Cancer with Mutation in the Human Epidermal Growth Factor Receptor 2 (HER2) And Machine Learning Technique

Gastric cancer with mutations in the human epidermal growth factor receptor (HER2) can be regarded as one of the leading causes of cancer mortality in the world. Targeted tyrosine kinase inhibitors (TKIs) developed against HER2 yielded optimistic results in improving patients' survival rates and life quality. Nevertheless, drug resistance can influence the critical supportive documents of treatment plans and decrease the treatment effectiveness after about one year. Predicting the efficacy of HER2-TKI drugs or therapies for patients with HER2-mutated gastric cancer is a critical research field. In the present study, a personalized drug response prediction model based on molecular dynamics simulations and machine learning is presented to predict response to first-generation drugs approved by the Ministry of Health in patients with gastric cancer. In the molecular dynamics simulation, the patient's mutation status is considered. The patient's unique mutation status was modeled using molecular dynamics simulations to extract geometric features at the molecular level.Furthermore, additional clinical features are incorporated into the machine learning model to predict drug response. The complete features encompass demographic and clinical characteristics, geometrical properties of the drug-target binding site, and binding free energy (RBFE) of the drug-target complex from molecular dynamics simulations. Drug response prediction utilizes the XGBoost classifier, which achieved leading-edge performance for a 4-level drug response prediction task (PDRP) with 97.5% accuracy, 93% sensitivity, 96.5% specificity, and 94% F1 score. The present research has demonstrated that modeling the binding cavity geometry, in tandem with the binding free energy, can effectively predict drug response. Interestingly, the clinical information, while significant, did not significantly influence the model's performance. This exciting finding opens up new avenues for testing the proposed model on various types of cancers, potentially revolutionizing drug development strategies. DOI: https://doi.org/10.52783/pst.391

Mesoporous silica nanoparticles as a drug delivery mechanism.

Research in intelligent drug delivery systems within the field of biomedicine promises to enhance drug efficacy at disease sites and reduce associated side effects. Mesoporous silica nanoparticles (MSNs), characterized by their large specific surface area, appropriate pore size, and excellent biocompatibility, have garnered significant attention as one of the most effective carriers for drug delivery. The hydroxyl groups on their surface are active functional groups, facilitating easy functionalization. The installation of controllable molecular machines on the surface of mesoporous silica to construct nanovalves represents a crucial advancement in developing intelligent drug delivery systems (DDSs) and addressing the issue of premature drug release. In this review, we compile several notable and illustrative examples of MSNs and discuss their varied applications in DDSs. These applications span regulated and progressive drug release mechanisms. MSNs hold the potential to enhance drug solubility, improve drug stability, and mitigate drug toxicity, attributable to their ease of functionalization. Furthermore, intelligent hybrid nanomaterials are being developed, featuring programmable properties that react to a broad spectrum of stimuli, including light, pH, enzymes, and redox triggers, through the use of molecular and supramolecular switches.

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have great potential to be used as porous adsorbents and membranes to achieve high-performance methane purification. Although the continuous increase in the number and diversity of MOFs and COFs is a great opportunity for the discovery of novel adsorbents and membranes with superior performances, evaluating such a vast number of materials in the quickest and most effective manner requires the development of computational approaches. High-throughput computational screening based on molecular simulations has been extensively used to identify the most promising MOFs and COFs for methane purification. However, the enormous and ever-growing material space necessitates more efficient approaches in terms of time and effort. Combining data science with molecular simulations has recently accelerated the discovery of optimal MOF and COF materials for methane purification and revealed the hidden structure–performance relationships. In this perspective, we highlighted the recent developments in combining high-throughput molecular simulations and machine learning to accurately identify the most promising MOF and COF adsorbents and membranes among thousands of candidates for separating methane from other gases including acetylene, carbon dioxide, helium, hydrogen, and nitrogen. After providing a brief overview of the topic, we reviewed the pioneering contributions in the field and discussed the current opportunities and challenges that we need to direct our efforts for the design and discovery of adsorbent and membrane materials.

Vogt-Koyanagi-Harada Syndrome Following COVID-19 mRNA Vaccination: Th2 to Th1 Transition-related Molecular Machinery.

Abstract is missing (Short communication)

Photoresponsive Biomimetic Functions by Light-Driven Molecular Motors in Three Dimensionally Printed Liquid Crystal Elastomers.

Despite the fascinating developments in design and synthesis of artificial molecular machines operating at the nanoscales, translating molecular motion along multiple length scales and inducing mechanical motion of a three-dimensional macroscopic entity remains an important challenge. The key to addressing this amplification of motion relies on the effective organization of molecular machines in a well-defined environment. By taking advantage of long-range orientational order and hierarchical structures of liquid crystals and unidirectional rotation of light-driven molecular motors, we report here photoresponsive biomimetic functions of liquid crystal elastomers (LCEs) by the repetitive unidirectional rotation of molecular motors using 3D printing. Molecular motors were built in the main chain of liquid crystals oligomers to serve as photoactuators. The oligomers were then used as the ink, and liquid crystal elastomers with different morphologies were printed. The obtained LCEs are able to conduct multiple types of motions including bending, helical coiling, closing of petals, and flipping of wings of a butterfly upon UV illumination, which paves the way for future design of responsive materials with enhanced complex actuating functions.

HERC3 facilitates ERAD of select membrane proteins by recognizing membrane-spanning domains.

Aberrant proteins located in the endoplasmic reticulum (ER) undergo rapid ubiquitination by multiple ubiquitin (Ub) E3 ligases and are retrotranslocated to the cytosol as part of the ER-associated degradation (ERAD). Despite several ERAD branches involving different Ub E3 ligases, the molecular machinery responsible for these ERAD branches in mammalian cells remains not fully understood. Through a series of multiplex knockdown/knockout experiments with real-time kinetic measurements, we demonstrate that HERC3 operates independently of the ER-embedded ubiquitin ligases RNF5 and RNF185 (RNF5/185) to mediate the retrotranslocation and ERAD of misfolded CFTR. While RNF5/185 participates in the ERAD process of both misfolded ABCB1 and CFTR, HERC3 uniquely promotes CFTR ERAD. In vitro assay revealed that HERC3 directly interacts with the exposed membrane-spanning domains (MSDs) of CFTR but not with the MSDs embedded in liposomes. Therefore, HERC3 could play a role in the quality control of MSDs in the cytoplasm and might be crucial for the ERAD pathway of select membrane proteins.

DNA-Based Molecular Machines: Controlling Mechanisms and Biosensing Applications.

The rise of DNA nanotechnology has driven the development of DNA-based molecular machines, which are capable of performing specific operations and tasks at the nanoscale. Benefitting from the programmability of DNA molecules and the predictability of DNA hybridization and strand displacement, DNA-based molecular machines can be designed with various structures and dynamic behaviors and have been implemented for wide applications in the field of biosensing due to their unique advantages. This review summarizes the reported controlling mechanisms of DNA-based molecular machines and introduces biosensing applications of DNA-based molecular machines in amplified detection, multiplex detection, real-time monitoring, spatial recognition detection, and single-molecule detection of biomarkers. The challenges and future directions of DNA-based molecular machines in biosensing are also discussed.

A distinct, high-affinity, alkaline phosphatase facilitates occupation of P-depleted environments by marine picocyanobacteria

Marine picocyanobacteria of the genera Prochlorococcus and Synechococcus, the two most abundant phototrophs on Earth, thrive in oligotrophic oceanic regions. While it is well known that specific lineages are exquisitely adapted to prevailing in situ light and temperature regimes, much less is known of the molecular machinery required to facilitate occupancy of these low-nutrient environments. Here, we describe a hitherto unknown alkaline phosphatase, Psip1, that has a substantially higher affinity for phosphomonoesters than other well-known phosphatases like PhoA, PhoX, or PhoD and is restricted to clade III Synechococcus and a subset of high light I-adapted Prochlorococcus strains, suggesting niche specificity. We demonstrate that Psip1 has undergone convergent evolution with PhoX, requiring both iron and calcium for activity and likely possessing identical key residues around the active site, despite generally very low sequence homology. Interrogation of metagenomes and transcriptomes from TARA oceans and an Atlantic Meridional transect shows that psip1 is abundant and highly expressed in picocyanobacterial populations from the Mediterranean Sea and north Atlantic gyre, regions well recognized to be phosphorus (P)-deplete. Together, this identifies psip1 as an important oligotrophy-specific gene for P recycling in these organisms. Furthermore, psip1 is not restricted to picocyanobacteria and is abundant and highly transcribed in some α-proteobacteria and eukaryotic algae, suggesting that such a high-affinity phosphatase is important across the microbial taxonomic world to occupy low-P environments.

Shape-Complementary Multicomponent Assembly of Low-Symmetry Co(III)Salphen-Based Coordination Cages.

While metal-mediated self-assembly is a popular technique to construct discrete nanosized objects, highly symmetric structures, built from one type of ligand at a time, are dominating reported systems. The tailored integration of a set of different ligands requires sophisticated approaches to avoid narcissistic separation or formation of statistical mixtures. Here, we demonstrate how the combination of three structure-guiding effects (metal-templated macrocyclization, additional bridging ligands and shape-complementarity) based on Co(III)salphen metal nodes allows for a rational and high-yielding synthesis of structurally complex, lantern-shaped cages with up to four differentiable bridges. Three new heteroleptic coordination cages based on dinuclear Co(III)salphen macrocycles were synthesized in a one-pot reaction approach and fully characterized, including single crystal X-ray analyses. One cage groups two of the same ligands, another two different ligands around a symmetric Co2-bis-salphen ring. In the most complex structure, this ring is unsymmetric, rendering all four connections between the two metal centers distinguishable. While heteroleptic assembly around Pd(II) nodes has been shown to be dynamic, beneficial for cage-to-cage transformations, assembly cascades and adaptive systems, the herein introduced cages based on kinetically more inert Co(III)salphen will be advantageous for applications in enzyme-like catalysis and molecular machinery that require enhanced structural and chemical stability.