Molecular Information Research Articles

Life cycle traits, secondary production and DNA barcode of Oxyurella ciliata Bergamin, 1939 (Crustacea, Branchiopoda, Anomopoda, Chydoridae)

This study the life cycle and quantify the secondary production of the cladoceran Oxyurella ciliata under controlled conditions in a laboratory and use molecular biology as a tool to investigate its genetic characteristics. The organisms were collected from Baia do Gerente, a pond from the Pantanal region, MS state, Brazil. They were acclimatized, maintained at a controlled temperature (25 ± 1 °C) and photoperiod (12/12h light-dark), fed with the microalgae Raphidocelis subcapitata and observed daily to obtain the data. O. ciliata had a high total egg production of 34.18 ± 9.68 eggs/female and an average longevity of 58.50 ± 16.30 days. These values differed from those previously reported for O. longicaudis, another congeneric species. There was an exponential growth of biomass until instar 6 and the largest secondary production was from the young to the adult phase, which corresponds to the beginning of the reproductive phase. The molecular data revealed that the genetic divergence between the sequence of O. ciliata and that of O. longicaudis is approximately 18 %, which seems high considering that both belong to the same genus. Comparing life cycle data and DNA Barcode, Oxyurella ciliata and O. longicaudis are very distant and have distinct morphological and biological characteristics, such as: body size, egg size, growth, fertility, longevity and development times. This study highlights the importance of molecular studies and information on the life cycle of neotropical cladocerans, in an integrated way, to have a better taxonomic and ecological interpretation of the species.

DMXP: A De Novo Small-Molecule 3D Structure Predictor with Graph Attention Networks.

Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such as 3D shape similarity search, 3D pharmacophore model, 3D-QSAR, etc., all require high-quality small-molecule ligand conformations to obtain reliable results. Although predicting a small molecular bioactive conformer requires information from the receptor, a crystal structure of the molecule is a proper approximation to its bioactive conformer in a specific receptor because the binding pose of a small molecule in its receptor's binding pockets should be energetically close to the crystal structures. This study presents a de novo small molecular structure predictor (dMXP) with graph attention networks based on crystal data derived from the Cambridge Structural Database (CSD) combined with molecular electrostatic information calculated by density-functional theory (DFT). Two featuring strategies (topological and atomic partial change features) were employed to explore the relation between these features and the 3D crystal structure of a small molecule. These features were then assembled to construct the holistic 3D crystal structure of a molecule. Molecular graphs were encoded using a graph attention mechanism to deal with the issues of the inconsistencies of local substructures contributing to the entire molecular structure. The root-mean-square deviation (RMSDs) of approximately 80% dMXP predicted structures and the native binding poses within receptors are less than 2.0 Å.

Repurposing Waste Chemicals for Sustainable and Durable Molecular Data Storage.

Molecular data storage offers the intriguing possibility of higher theoretical density and longer lifetimes than today's electronic memory devices. Some demonstrations have used deoxyribonucleic acid (DNA), but bottlenecks in nucleic acid synthesis continue to make DNA data storage orders of magnitude more expensive than electronic storage media. Additionally, despite its potential for long-term storage, DNA faces durability challenges from environmental degradation. In this work, we demonstrate nongenomic molecular data storage using molecular libraries redirected from chemical waste streams. This approach requires no synthetic effort and can be implemented by using molecules that have a minimal associated cost. While the technique is agnostic about the exact molecular content of its inputs, we confirmed that some sources contained poly fluoroalkyl substances (PFAS), which persist for long periods in the natural environment and could offer extremely durable information storage as well as environmental benefits. These demonstrations provide a perspective on some of the valuable possibilities for nongenomic molecular information systems.

Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions.

In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.

From Local Atomic Environments to Molecular Information Entropy.

The similarity of local atomic environments is an important concept in many machine learning techniques, which find applications in computational chemistry and material science. Here, we present and discuss a connection between the information entropy and the similarity matrix of a molecule. The resulting entropy can be used as a measure of the complexity of a molecule. Exemplarily, we introduce and evaluate two specific choices for defining the similarity: one is based on a SMILES representation of local substructures, and the other is based on the SOAP kernel. By tuning the sensitivity of the latter, we can achieve good agreement between the respective entropies. Finally, we consider the entropy of two molecules in a mixture. The gain of entropy due to the mixing can be used as a similarity measure of the molecules. We compare this measure to the average and best-match kernel. The results indicate a connection between the different approaches and demonstrate the usefulness and broad applicability of the similarity-based entropy approach.

Conformation Influence of DNA on the Detection Signal through Solid-State Nanopores.

The detection and identification of nanoscale molecules are crucial, but traditional technology comes with a high cost and requires skilled operators. Solid-state nanopores are new powerful tools for discerning the three-dimensional shape and size of molecules, enabling the translation of molecular structural information into electric signals. Here, DNA molecules with different shapes were designed to explore the effects of electroosmotic forces (EOF), electrophoretic forces (EPF), and volume exclusion on electric signals within solid-state nanopores. Our results revealed that the electroosmotic force was the main driving force for single-stranded DNA (ssDNA), whereas double-stranded DNA (dsDNA) was primarily dominated by electrophoretic forces in nanopores. Moreover, dsDNA caused greater amplitude signals and moved faster through the nanopore due to its larger diameter and carrying more charges. Furthermore, at the same charge level and amount of bases, circular dsDNA exhibited a tighter structure compared to brush DNA, resulting in a shorter length. Consequently, circular dsDNA caused higher current-blocking amplitudes and faster passage speeds. The characterization approach based on nanopores allows researchers to get molecular information about size and shape in real time. These findings suggest that nanopore detection has the potential to streamline nanoscale characterization and analysis, potentially reducing both the cost and complexity.

Precision oncology and molecular tumor boards

Precision oncology is afield of personalized medicine in which tumor biology forms the basis for tailored treatments. The preferred approach currently applied in clinical practice is based on the concept of malignant tumors as genetic diseases that are caused by mutations in oncogenes and tumor suppressors. On the one hand, these can be targeted by molecular drugs, while on the other hand, next-generation sequencing allows for comprehensive analysis of all relevant aberrations, thus enabling the matching of appropriate treatments across entities based on molecular information. Rational molecular therapies are developed and annotated with supporting evidence by molecular tumor boards, which have been established at various academic centers in recent years. Advancing precision oncology to anew standard of care requires improved applicability of personalized molecular therapies and thorough scientific evaluation of precision oncology programs.

Viewing early life without labels: optical approaches for imaging the early embryo.

Embryo quality is an important determinant of successful implantation and a resultant live birth. Current clinical approaches for evaluating embryo quality rely on subjective morphology assessments or an invasive biopsy for genetic testing. However, both approaches can be inherently inaccurate and crucially, fail to improve the live birth rate following the transfer of in vitro produced embryos. Optical imaging offers a potential non-invasive and accurate avenue for assessing embryo viability. Recent advances in various label-free optical imaging approaches have garnered increased interest in the field of reproductive biology due to their ability to rapidly capture images at high-resolution, delivering both morphological and molecular information. This burgeoning field holds immense potential for further development, with profound implications for clinical translation. Here, our review aims to: 1) describe the principles of various imaging systems, distinguishing between approaches that capture morphological and molecular information, 2) highlight the recent application of these technologies in the field of reproductive biology, and 3) assess their respective merits and limitations concerning the capacity to evaluate embryo quality. Additionally, the review summarizes challenges in the translation of optical imaging systems into routine clinical practice, providing recommendations for their future development. Finally, we identify suitable imaging approaches for interrogating the mechanisms underpinning successful embryo development.

Alterations in lipid metabolism accompanied by changes in protein and carotenoid content as spectroscopic markers of human T cell activation

This work aims to understand better the mechanism of cellular processes accompanying the activation of human T cells and to develop a novel, fast, label-free approach to identify molecular biomarkers for this process. The standard methodology for confirming the activation state of T cells is based on flow cytometry and using antibodies recognizing activation markers. The method provide high specificity detection but may be susceptible to background staining or non-specific secondary antibody reactions. Here, we evaluated the potential of Raman-based molecular imaging in distinguishing non-activated and activated human T cells. Confocal Raman microscopy was performed on T cells followed by chemometrics to obtain comprehensive molecular information, while Stimulated Raman Scattering imaging was used to quickly provide high-resolution images of selected cellular components of activated and non-activated cells. For the first time, carotenoids, lipids, and proteins were shown to be important biomarkers of T-cell activation. We found that T-cell activation was accompanied by lipid accumulation and loss of carotenoid content. Our findings on the biochemical, morphological, and structural changes associated with activated mature T cells provide insights into the molecular changes that occur during therapeutic manipulation of the immune response. The methodology for identifying activated T cells is based on a novel imaging method and supervised and unsupervised chemometrics. It unambiguously identifies specific and unique molecular changes without the need for staining, fixation, or any other sample preparation.

Analysis of the effect of cations on protein conformational stability using solid-state nanopores.

The conformation of proteins is closely related to their biological functions, and it is affected by many factors, including the type of cations in solution. However, it is difficult to detect the conformational changes of a protein in situ. As a single-molecule sensing technology, nanopores can convert molecular structural information into analyzable current signals within a reasonable time range. Herein, we detect and analyze the effects of two different types of monovalent cations (Na+ and Li+) on a model protein bovine serum albumin (BSA) conformation using SiNx nanopores with different diameters. The quantitative analysis results show that the excluded volume of BSA in LiCl salt solutions is larger than the value in NaCl solution, indicating that Li+ is more prone to unfolding the proteins and making them unstable. This study demonstrated that nanopores enable the in situ detection of the structure of proteins at the single-molecule level and provide a new approach for the quantitative analysis of proteins.

The Role of Sum-Frequency Generation Spectroscopy in Understanding On-Surface Reactions and Dynamics in Atmospheric Model-Systems.

Surfaces, both water/air and solid/water, play an important role in mediating a multitude of processes central to atmospheric chemistry, particularly in the aerosol phase. However, the study of both static and dynamic properties of surfaces is highly challenging from an experimental standpoint, leading to a lack of molecular level information about the processes that take place at these systems and how they differ from bulk. One of the few techniques that has been able to capture ultrafast surface phenomena is time-resolved sum-frequency generation (SFG) spectroscopy. Since it is both surface-specific and chemically sensitive, the extension of this spectroscopic technique to the time domain makes it possible to study dynamic processes on the femtosecond time scale. In this Perspective, we will explore recent advances made in the field both in terms of studying energy dissipation as well as chemical reactions and the role the surface geometry plays in these processes.

Multiome in the Same Cell Reveals the Impact of Osmotic Stress on Arabidopsis Root Tip Development at Single-Cell Level.

Cell-specific transcriptional regulatory networks (TRNs) play vital roles in plant development and response to environmental stresses. However, traditional single-cell mono-omics techniques are unable to directly capture the relationships and dynamics between different layers of molecular information within the same cells. While advanced algorithm facilitates merging scRNA-seq and scATAC-seq datasets, accurate data integration remains a challenge, particularly when investigating cell-type-specific TRNs. By examining gene expression and chromatin accessibility simultaneously in 16,670 Arabidopsis root tip nuclei, the TRNs are reconstructed that govern root tip development under osmotic stress. In contrast to commonly used computational integration at cell-type level, 12,968 peak-to-gene linkage is captured at the bona fide single-cell level and construct TRNs at an unprecedented resolution. Furthermore, the unprecedented datasets allow to more accurately reconstruct the coordinated changes of gene expression and chromatin states during cellular state transition. During root tip development, chromatin accessibility of initial cells precedes gene expression, suggesting that changes in chromatin accessibility may prime cells for subsequent differentiation steps. Pseudo-time trajectory analysis reveal that osmotic stress can shift the functional differentiation of trichoblast. Candidate stress-related gene-linked cis-regulatory elements(gl-cCREs) as well as potential target genes are also identified, and uncovered large cellular heterogeneity under osmotic stress.

Molecular robotic agents that survey molecular landscapes for information retrieval

DNA-based artificial motors have allowed the recapitulation of biological functions and the creation of new features. Here, we present a molecular robotic system that surveys molecular environments and reports spatial information in an autonomous and repeated manner. A group of molecular agents, termed ‘crawlers’, roam around and copy information from DNA-labeled targets, generating records that reflect their trajectories. Based on a mechanism that allows random crawling, we show that our system is capable of counting the number of subunits in example molecular complexes. Our system can also detect multivalent proximities by generating concatenated records from multiple local interactions. We demonstrate this capability by distinguishing colocalization patterns of three proteins inside fixed cells under different conditions. These mechanisms for examining molecular landscapes may serve as a basis towards creating large-scale detailed molecular interaction maps inside the cell with nanoscale resolution.

Activatable near-infrared fluorescence and chemical exchange saturation transfer MRI multimodal imaging probe for tumor detection in vitro and in vivo

Multimodal imaging has emerged as a powerful tool in biomedical research and clinical diagnostics. Ideally, it combines multiple imaging techniques to provide complementary anatomical and molecular information in living subjects. Particularly desirable are multimodal imaging probes capable of providing differential diagnostic signals upon interaction with specific molecular targets. Hydrogen peroxide (H2O2) is a key target in this regard, since it is typically overexpressed in cancer cells. In this study, we present a small-molecule probe that not only selectively detects endogenous H2O2 through multimodal imaging, with a significant H2O2-triggered 15-fold fluorescence enhancement but also turns “on” a chemical exchange saturation transfer (CEST) magnetic resonance (MR) response with 60-fold signal enhancement at pH 7.4. Excellent selectivity against various other biologically relevant species is seen. Using this probe, we observed 3.4–4.5-fold and 2.8–5.8-fold higher H2O2 levels in cancerous cell lines and tumor tissues compared to normal cell lines and tissues, respectively. Time-dependent in vivo fluorescence and CEST imaging in a HeLa (Henrietta Lacks) tumor xenograft mouse model revealed probe-dependent tumor detection by fluorescence and CEST MRI contrast in the tumor area. These observations are attributed to the relatively high endogenous H2O2 levels produced during mitosis. This newly developed probe holds promise for advancing our understanding of H2O2-related biology and for cancer detection both in vitro and in vivo.

Engineering Modular DNA Reaction Networks for Signal Processing.

Diversified molecular information-processing methods have significant implications for nanoscale manipulation and control, monitoring and disease diagnosis of organisms, and direct intervention in biological activities. However, as an effective approach for implementing multifunctional molecular information processing, DNA reaction networks (DRNs) with numerous functionally specialized molecular structures have challenged them on scale and modular design, leading to increased network complexity, further causing problems such as signal leakage, attenuation, and cross-talk in network reactions. Our study developed a strategy for performing various signal-processing tasks through engineering modular DRNs composed of simple molecular structures. This strategy is based on a universal core unit with signal selection capability, and a timeadjustable signal self-resetting module is achieved by combing the core unit and self-resetting unit, which improves the time controllability of modular DRNs. In addition, multi-input and -output signal crosscatalytic and continuously adjustable signal delay modules were realized by combining core and threshold units, providing a flexible, precise method for modular DRNs to process the signal. The strategy simplifies the design of DRNs, helps generate design ideas for largescale integrated DRNs with multiple functions, and provides prospects in biocomputing, gene regulation, and biosensing.

Complexation mechanism of Pb2+ on Al-substituted hematite: A modeling study and theoretical calculation

Hematite nanoparticles commonly undergoes isomorphic substitution of Al3+ in nature, while how the Al-substitution-induced morphological change, defective structure and newly generated Al–OH sites affect the adsorption behavior of hematite for contaminants remains poorly understood. Herein, the interfacial reactions between Al-substituted hematite and Pb2+ was investigated via CD-MUSIC modeling and DFT calculations. As the Al content increased from 0% to 9.4%, Al-substitution promoted the proportion of (001) facets and caused Fe vacancies on hematite, which increased the total active site density of hematite from 5.60 to 17.60 sites/nm2. The surface positive charge of hematite significantly increased from 0.096 to 0.418 C/m2 at pH 5.0 due to the increases in site density and proton affinity (logKH) of hematite under Al-substitution. The adsorption amount of hematite for Pb2+ increased from 3.92 to 9.74 mmol/kg at pH 5.0 and 20 μmol/L initial Pb2+ concentration with increasing Al content. More Fe vacancies may lead to a weaker adsorption energy (Ead) of hematite for Pb2+, while the Ead was enhanced at higher Al content. The adsorption affinity (logKPb) of bidentate Pb complexes slightly increased while that of tridentate Pb complexes decreased with increasing Al content due to the presence of ≡ AlOH−0.5 and ≡ Fe2AlO−0.5 sites. Tridentate Pb complexes were dominant species on the surface of pure hematite, while bidentate ones became more dominant with increasing Al content. The obtained model parameters and molecular scale information are of great importance for better describing and predicting the environmental fate of toxic heavy metals in terrestrial and aquatic environments.

Integrating Multi-omics Data for Alzheimer's Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint Deep Semi-Non-Negative Matrix Factorization Algorithm.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF .

New CNS tumor classification: The importance in pediatric neurosurgical practice.

The management of the central nervous system (CNS) tumors in the pediatric population is crucial in neurosurgical practice. The World Health Organization (WHO) has evolved its classification of CNS tumors from the 19th century to the 5th edition, published in 2021, incorporating molecular advancements. This transition from morphology to molecular characterization is ongoing. This manuscript analyzes the modifications introduced in the 5th edition of WHO's CNS tumor classification, particularly focusing on pediatric tumor families. The paper integrates clinical, morphological, and molecular information, aiming to guide pediatric neurosurgeons in their daily practice and interdisciplinary discussions. The 5th edition of the WHO classification introduces a hybrid taxonomy that incorporates both molecular and histological components. The terminology shifts from "entity" to "type" and "subtype," aiming to standardize terminology. Tumor grading experiences changes, integrating molecular biomarkers for prognosis. The concept of integrated layered diagnosis is emphasized, where molecular and histological information is combined systematically. The 5th edition of the WHO CNS classification signifies a paradigm shift toward molecular characterization. The incorporation of molecular advances, the layered diagnostic approach, and the inclusion of clinical, morphological, and molecular information aim to provide comprehensive insights into pediatric CNS tumors. This classification offers valuable guidance for pediatric neurosurgeons, aiding in precise diagnosis and treatment planning for these complex neoplasms.

Reducing Ice Adhesion to Polyelectrolyte Surfaces by Counterion-Mediated Nonfrozen Hydration Water.

Hydrophilic anti-icing coatings can be energy-effective passive solutions for combating ice accretion and reducing ice adhesion. However, their underlying mechanisms of action remain inferential and are ill-defined from a molecular perspective. Here, we systematically investigate the influence of the counterion identity on the shear ice adhesion strength to cationic polymer coatings having quaternary alkyl ammonium moieties as chargeable groups. Temperature-dependent molecular information on the hydrated polymer films is obtained using total internal reflection (TIR) Raman spectroscopy, complemented with differential scanning calorimetry (DSC) and ellipsometry. Ice adhesion measurements show a pronounced counterion-specific behavior with a sharp increase in adhesion at temperatures that depend on the anion identity, following the order Cl- < F- < SCN- < Br- < I-. Linked to the freezing of hydration water, the specific ordering results from differences in ion pairing and the amount of water present within the polymer film. Moreover, similar effects can be promoted by varying the cross-linking density in the coating while keeping the anion identity fixed. These findings shed new light on low ice adhesion mechanisms and may inspire novel approaches for improved anti-icing coatings.

Chemical space deconstruction-based dynamic model ensemble architecture for molecular property prediction

Green solvents, catalysts, functional materials, drugs, and other chemical products now have a quick engine for design thanks to machine learning (ML) based prediction of molecular properties. However, the accuracy and stability of ML-based models can be impeded by poor data quality, which is rarely studied in chemical product discovery and design. Inspired by the dynamic ensemble selection (DES), an improved DES based on chemical space deconstruction is proposed in this work to accommodate the prediction task of molecular properties. We innovatively developed a chemical space representation and deconstruction model based on a self-organizing mapping (SOM) neural network, facilitating the rapid implementation of the improved DES on molecular samples. Consequently, a novel dynamic model ensemble architecture (SOM-DES) is proposed as a model enhancement technology to build a more accurate and stable ensemble model, aiming to improve the predictive performance on the chemical subspace within poor-quality data. To achieve the architecture, a supervised dimensionality reduction algorithm has been improved to enhance the deep mining of molecular feature information for DES optimization. Additionally, a novel resampling strategy based on the combination of the geometric synthetic minority oversampling technique (G-SMOTE) algorithm and chemical space deconstruction, as a data augmentation technology, has been proposed for mitigating the disadvantage of unbalanced data during DES training. The prediction task for enthalpy of formation of the ideal gas is applied as a case study to demonstrate the superiority of the proposed SOM-DES. The results indicate that the proposed SOM-DES (R2 = 0.9731, RMSE = 55.4639) outperforms the traditional static ensemble strategy (SES, R2 = 0.9552, RMSE = 71.5045) in terms of global chemical spatial precision. More importantly, for chemical subspaces that are difficult to predict due to low data quality, SOM-DES shows a significant reduction in prediction errors compared to SES.