Browse Articles

A distributed subcortical circuit linked to instrumental information-seeking about threat

Daily life for humans and other animals requires switching between periods of threat- and reward-oriented behavior. We investigated neural activity associated with spontaneous switching, in a naturalistic task, between foraging for rewards and seeking information about potential threats with 7T fMRI in healthy humans. Switching was driven by estimates of likelihood of threat and reward. Both tracking of threat and switching to a vigilant mode in which people sought more information about potential threats were associated with specific but distributed patterns of activity spanning habenula, dorsal raphe nucleus (DRN), anterior cingulate cortex, and anterior insula cortex. Different aspects of the distributed activity patterns were linked to monitoring the threat level, seeking information about the threat, and actual threat detection. A distinct pattern of activity in the same circuit and elsewhere occurred during returns to reward-oriented behavior. Individual variation in DRN activity reflected individual variation in the seeking of information about threats.

Molecular conductance calculations of single-molecule junctions using projection-based density functional embedding

Single-Molecule Junctions (SMJs) are key platforms for the exploration of electron transport at the molecular scale. In this study, we present a method that employs different exchange-correlation density functionals for the molecule and the lead domains in an SMJ, enabling the selection of the optimal one for each part. This is accomplished using a formally exact projection-based density-functional theory (DFT-in-DFT) embedding technique combined with the non-equilibrium Green’s function method to predict zero-bias conductance. The effectiveness of this approach is illustrated through transport calculations on SMJs with benzene-1,4-diamine and its tetramethylated and tetrafluorinated variants, using the CAM-B3LYP range-separated hybrid functional for the embedded molecule and the Perdew–Burke–Ernzerhof (PBE) functional for the electrodes. The findings indicate a substantial improvement in the accuracy of the predicted zero-bias conductance compared to traditional modeling using the PBE functional across the entire system. The causes for the noted improvement are demonstrated through the examination of alterations in the energy levels of the embedded molecule, along with variations in the electrode–molecule interactions.

Soil microbiome bacteria protect plants against filamentous fungal infections via intercellular contacts

Bacterial–fungal interaction (BFI) has significant implications for the health of host plants. While the diffusible antibiotic metabolite-mediated competition in BFI has been extensively characterized, the impact of intercellular contact remains largely elusive. Here, we demonstrate that the intercellular contact is a prevalent mode of interaction between beneficial soil bacteria and pathogenic filamentous fungi. By generating antibiotics-deficient mutants in two common soil bacteria, Lysobacter enzymogenes and Pseudomonas fluorescens , we show that antibiotics-independent BFI effectively inhibits pathogenic fungi. Furthermore, transcriptional and genetic evidence revealed that this antibiotics-independent BFI relies on intercellular contact mediated by the type VI secretion system (T6SS), which may facilitate the translocation of bacterial toxic effectors into fungal cells. Finally, by using a “conidia enrichment” platform, we found that T6SS-mediated fungal inhibition resulting from intercellular contact naturally occurs within the soil microbiome, particularly represented by Pseudomonas fulva . Overall, these results demonstrate that bacteria from the soil microbiome can protect host plants from fungal infection through antibiotics-independent intercellular contacts, thus revealing a naturally occurring and ecologically important mode of BFI in agricultural contexts.

Uncovering dissipation from coarse observables: A case study of a random walk with unobserved internal states

Inferring underlying microscopic dynamics from low-dimensional experimental signals is a central problem in physics, chemistry, and biology. As a trade-off between molecular complexity and the low-dimensional nature of experimental data, mesoscopic descriptions such as the Markovian master equation are commonly used. The states in such descriptions usually include multiple microscopic states, and the ensuing coarse-grained dynamics are generally non-Markovian. It is frequently assumed that such dynamics can nevertheless be described as a Markov process because of the timescale separation between slow transitions from one observed coarse state to another and the fast interconversion within such states. Here, we use a simple model of a molecular motor with unobserved internal states to highlight that (1) dissipation estimated from the observed coarse dynamics may significantly underestimate microscopic dissipation even in the presence of timescale separation and even when mesoscopic states do not contain dissipative cycles and (2) timescale separation is not necessarily required for the Markov approximation to give the exact entropy production, provided that certain constraints on the microscopic rates are satisfied. When the Markov approximation is inadequate, we discuss whether including memory effects can improve the estimate. Surprisingly, when we do so in a “model-free” way by computing the Kullback–Leibler divergence between the observed probability distributions of forward trajectories and their time reverses, this leads to poorer estimates of entropy production. Finally, we argue that alternative approaches, such as hidden Markov models, may uncover the dissipative nature of the microscopic dynamics even when the observed coarse trajectories are completely time-reversible.

Direct lysine dimethylation of IRF3 by the methyltransferase SMYD3 attenuates antiviral innate immunity

Interferon regulatory factor 3 (IRF3) is the key transcription factor in the type I IFN signaling pathway, whose activation is regulated by multiple posttranslational modifications. Here, we identify SMYD3, a lysine methyltransferase, as a negative regulator of IRF3. SMYD3 interacts with IRF3 and catalyzes the dimethylation of IRF3 at lysine 39. This modification reduces IRF3 phosphorylation, dimerization, and subsequent nuclear translocation, leading to the inhibition of downstream type I interferon production. In addition, Smyd3 -deficient mice are more resistant to RNA and DNA viral infections. Zebrafish lacking smyd3 or treated with the inhibitor BCI121 are also more resistant to viral infection. Our findings reveal a role for SMYD3 in the regulation of antiviral innate immunity and provide insight into a specific modulation of IRF3 that affects its activation.

Non-adiabatic quantum electrodynamic effects on electron–nucleus–photon systems: Single photonic mode vs infinite photonic modes

The quantum-electrodynamic non-adiabatic emission (QED-NAE) is a type of radiatively assisted vibronic de-excitation due to electromagnetic vacuum fluctuations on non-adiabatic processes. Building on our previous work [Tsai et al., J. Phys. Chem. Lett. 14, 5924 (2023)], we extend the theory of the QED-NAE rate from a single cavity photonic mode to infinite photonic modes and calculate the QED-NAE rates of 9-cyanoanthracene at the first-principles level. To avoid the confusion, the quantum electrodynamic internal conversion process is renamed as “QED-NAE” in our present work. According to our theory, we identify three key factors influencing the QED-NAE processes: light–matter coupling strength (mode volume), mass-weighted orientation factor, and photonic density of states. The mode volume is the primary factor causing rate differences between the two scenarios. In a single cavity with a small mode volume, strong light–matter coupling strength boosts QED-NAE rates. In contrast, in free space with infinite photonic modes, weak coupling strength significantly reduces these rates. From a single cavity photonic mode to infinite photonic modes, the mass-weighted orientation factor only causes an 8π/3-fold increase in the QED-NAE rate. In free space, the photonic density of state exhibits a flat and quadratic distribution, which slightly reduces the QED-NAE rate. Our study shows that cavities can significantly enhance non-adiabatic QED effects while providing a robust analysis demonstrating that QED vibronic effects can be safely ignored in free space.

A histochemical approach to activity-based copper sensing reveals cuproplasia-dependent vulnerabilities in cancer

Copper is an essential nutrient for sustaining vital cellular processes spanning respiration, metabolism, and proliferation. However, loss of copper homeostasis, particularly misregulation of loosely bound copper ions which are defined as the labile copper pool, occurs in major diseases such as cancer, where tumor growth and metastasis have a heightened requirement for this metal. To help decipher the role of copper in the etiology of cancer, we report a histochemical activity-based sensing approach that enables systematic, high-throughput profiling of labile copper status across many cell lines in parallel. Coppermycin-1 reacts selectively with Cu(I) to release puromycin, which is then incorporated into nascent peptides during protein translation, thus leaving a permanent and dose-dependent marker for labile copper that can be visualized with standard immunofluorescence assays. We showcase the utility of this platform for screening labile Cu(I) pools across the National Cancer Institute’s 60 (NCI-60) human tumor cell line panel, identifying cell types with elevated basal levels of labile copper. Moreover, we use Coppermycin-1 to show that lung cancer cells with heightened activation of nuclear factor-erythroid 2-related factor 2 (NRF2) possess lower resting labile Cu(I) levels and, as a result, have reduced viability when treated with a copper chelator. This work establishes that methods for labile copper detection can be used to assess cuproplasia, an emerging form of copper-dependent cell growth and proliferation, providing a starting point for broader investigations into the roles of transition metal signaling in biology and medicine.

Higher-order effects and validity of the point-dipole approximation for conjugated extended molecular emitters near plasmonic nanostructures

Rapid advancements in nanotechnology have allowed for the characterization of single molecules by placing them in the vicinity of nanoplasmonic structures that are known to confine light to sub-molecular scales. In this study, we introduce a theoretical framework that captures higher-order effects, and we explore the limits of the standard description of a molecular emitter as a point-dipole. We particularly focus on the role played by the emitter chain length and electron conjugation. Strong deviations are observed from the point-dipole approximation, demonstrating that higher-order effects are essential to fully capture the emission rate of extended molecules in the vicinity of nanoparticles. This deviation strongly depends on the orientation of the conjugated chain relative to the nanoplasmonic structure. Finally, we propose a simple rationalization that qualitatively assesses the difference from the point-dipole approximation.

Estimating realized relatedness in free-ranging macaques by inferring identity-by-descent segments

Biological relatedness is a key consideration in studies of behavior, population structure, and trait evolution. Except for parent–offspring dyads, pedigrees capture relatedness imperfectly. The number and length of identical-by-descent DNA segments (IBD) yield the most precise relatedness estimates. Here, we leverage different methods for estimating IBD segments from low-depth whole genome resequencing data to demonstrate the feasibility and value of resolving fine-scaled gradients of relatedness in free-living animals. Using primarily 4 to 6× depth data from a rhesus macaque ( Macaca mulatta ) population with long-term pedigree data, we show that we can infer the number and length of IBD segments across the genome with high accuracy even at 0.5× sequencing depth. In line with expectations based on simulation, the resulting estimates demonstrate substantial variation in genetic relatedness within kin classes, leading to overlapping distributions between kin classes. By comparing the IBD-based estimates with pedigree and short tandem repeat-based methods, we show that IBD estimates are more reliable and provide more detailed information on kinship. The inferred IBD segments also identify cryptic genetic relatives not represented in the pedigree and reveal elevated recombination rates in females relative to males, which enables the majority of close maternal and paternal kin to be distinguished with genotype data alone. Our findings represent a breakthrough in the ability to study the predictors and consequences of genetic relatedness in natural populations, contributing to our understanding of a fundamental component of population structure in the wild.

The low-lying electronic states of 4H-pyran-4-thione; a photoionization and vacuum ultraviolet absorption study, with interpretation by configuration interaction and density functional calculations for the ionic and singlet states

Two synchrotron-based studies on 4H-pyran-4-thione, photoelectron spectroscopy and vacuum ultraviolet (VUV) absorption spectra were performed. A highly resolved structure was observed in the photoelectron spectrum (PES), in contrast to an earlier PES study, where little structure was observed. The sequence of ionic states was determined using configuration interaction and coupled cluster methods. The vibrational structure of the lowest three PES bands was analyzed by configuration interaction and density functional calculations, providing a detailed explanation of the observed profiles. Several vibrational bands in the VUV absorption spectrum showed a similar structure to the bands in the PES and were identified as Rydberg states.

Specialization of the human hippocampal long axis revisited

The hippocampus possesses anatomical differences along its long axis. Here, we explored the functional specialization of the human hippocampal long axis using network-anchored precision functional MRI in two independent datasets (N = 11 and N = 9) paired with behavioral analysis (N = 266 and N = 238). Functional connectivity analyses demonstrated that the anterior hippocampus was preferentially correlated with a cerebral network associated with remembering, while the posterior hippocampus selectively contained a region correlated with a distinct network associated with behavioral salience. Seed regions placed within the hippocampus recapitulated the distinct cerebral networks. Functional characterization of the anterior and posterior hippocampal regions using task data identified and replicated a functional double dissociation. The anterior hippocampal region was sensitive to remembering and imagining the future, specifically tracking the process of scene construction, while the posterior hippocampal region displayed transient responses to targets in an oddball detection task and to transitions between task blocks. These findings suggest an unexpected specialization along the long axis of the human hippocampus with differential responses reflecting the functional properties of the partner cerebral networks.

Plasma decomposition of ferrocene

Ferrocene [Fe(C5H5)2 or FeCp2] is a well-known precursor molecule for iron in vapor deposition of iron containing films by, e.g., chemical vapor deposition (CVD) processes. CVD processes often use the energy in plasma discharges to decompose precursor molecules, which allows lowering the substrate temperature for deposition on sensitive materials. Herein, we studied the plasma decomposition of ferrocene in a plasma CVD reactor using in situ optical emission spectroscopy and quadrupole mass spectrometry, coupled with in silico quantum chemical modeling. We suggest a plasma chemical decomposition model under medium vacuum conditions where FeCp2 is likely to undergo neutral decomposition, detaching both Cp ligands from the iron center, followed by fragmentation via C2H2− and C3H3 to C2, CH, H2, and H.

Tubulin detyrosination shapes <i>Leishmania</i> cytoskeletal architecture and virulence

Tubulin detyrosination has been implicated in various human disorders and is important for regulating microtubule dynamics. While in most organisms this modification is restricted to α-tubulin, in trypanosomatid parasites, it occurs on both α- and β-tubulin. Here, we show that in Leishmania , a single vasohibin (LmVASH) enzyme is responsible for differential kinetics of α- and β-tubulin detyrosination. LmVASH knockout parasites, which are completely devoid of detyrosination, show decreased levels of glutamylation and exhibit a strongly diminished pathogenicity in mice, correlating with decreased proliferation in macrophages. Reduced virulence is associated with altered morphogenesis and flagellum remodeling in detyrosination-deficient amastigotes. Flagellum shortening in the absence of detyrosination is caused by hyperactivity of a microtubule-depolymerizing Kinesin-13 homolog, demonstrating its function as a key reader of the trypanosomatid-tubulin code. Taken together, our work establishes the importance of tubulin detyrosination in remodeling the microtubule-based cytoskeleton required for efficient proliferation in the mammalian host. This highlights tubulin detyrosination as a potential target for therapeutic action against leishmaniasis.

Flexible framework of computing binding free energy using the energy representation theory of solution

Host–guest binding plays a crucial role in the functionality of various systems, and its efficiency is often quantified using the binding free energy, which represents the free-energy difference between the bound and dissociated states. Here, we propose a methodology to compute the binding free energy based on the energy representation (ER) theory of solution, which enables us to evaluate the free-energy difference between the systems of interest with the molecular dynamics (MD) simulations. Unlike the other free-energy methods, such as the Bennett acceptance ratio (BAR), the ER theory does not require the MD simulations for hypothetical intermediate states connecting the systems of interest, leading to reduced computational costs. By constructing the thermodynamic cycle of the binding process that is suitable for the ER theory, a robust calculation of the binding free energy is realized. We apply the present method to the self-association of N-methylacetamide in different solvents and the binding of aspirin to β-cyclodextrin (CD) in water. In the former case, the present method estimates that the binding free energy decreases as the solvent polarity decreases. This trend is consistent with the experimental finding. For the latter system, the binding free energies for the two representative CD–aspirin bound complexes, primary (P) and secondary (S) complexes, are estimated to be −5.2 ± 0.1 and −5.03 ± 0.09 kcal mol−1, respectively. These values are satisfactorily close to those from the BAR method [−4.2 ± 0.2 and −4.1 ± 0.2 kcal mol−1 for P and S, respectively]. Furthermore, the interaction-energy component analysis reveals that the van der Waals interaction between aspirin and CD dominantly contributes to the stabilization of the bound complexes, which is in harmony with the well-known binding mechanism in the CD systems.

The chromatin remodeler ADNP regulates neurodevelopmental disorder risk genes and neocortical neurogenesis

Although chromatin remodelers are among the most important risk genes associated with neurodevelopmental disorders (NDDs), the roles of these complexes during brain development are in many cases unclear. Here, we focused on the recently discovered ChAHP chromatin remodeling complex. The zinc finger and homeodomain transcription factor ADNP is a core subunit of this complex, and de novo ADNP mutations lead to intellectual disability and autism spectrum disorder. However, germline Adnp knockout mice were previously shown to exhibit early embryonic lethality, obscuring subsequent roles for the ChAHP complex in neurogenesis. To circumvent this early developmental arrest, we generated a conditional Adnp mutant allele. Using single-cell transcriptomics, cut&amp;run-seq, and histological approaches, we show that during neocortical development, Adnp orchestrates the production of late-born, upper-layer neurons through a two-step process. First, Adnp is required to sustain progenitor proliferation specifically during the developmental window for upper-layer cortical neurogenesis. Accordingly, we found that Adnp recruits the ChAHP subunit Chd4 to genes associated with progenitor proliferation. Second, in postmitotic differentiated neurons, we define a network of risk genes linked to NDDs that are regulated by Adnp and Chd4. Taken together, these data demonstrate that ChAHP is critical for driving the expansion of upper-layer cortical neurons and for regulating neuronal gene expression programs, suggesting that these processes may potentially contribute to NDD etiology.

Self-consistent electron density with shell structure using neural network-based Pauli potential

The orbital-free density functional theory (OF-DFT) based method is a convenient tool to carry out electronic structure calculations scaling almost linearly with the number of electrons. However, the main impediment in the application of this method is the unavailability of the accurate form for the non-interacting kinetic energy functional in terms of electron density. The Pauli kinetic energy functional is the unknown part of the kinetic energy functional, and the corresponding Pauli potential appears in the governing Euler equation. In the present study, we present a feed-forward neural network (NN) approach to represent the Pauli potential of a group of atomic systems possessing spherically symmetric ground-state densities. This NN-based representation of Pauli potential combined with the Hohenberg–Kohn variational principle yields self-consistent radial densities that accurately exhibit the correct atomic shell structure. For this approach, the electron density in the form of a grid serves as the input to the NN model. In addition, we calculated the non-interacting kinetic energy by summing the Pauli kinetic energy, derived from the NN-based Pauli potential, and the von Weizsäcker kinetic energy. Our results demonstrate high accuracy for smaller atoms, while larger atoms exhibit greater deviations when compared with smaller atoms. The method presented in this paper provides an efficient way to calculate the Pauli potential and the Pauli kinetic energy without the need for functional derivatives. Our study represents a significant step forward in the application of machine learning techniques to OF-DFT, showcasing the potential of NNs in improving the accuracy and efficiency of quantum mechanical calculations in atomic systems.

ADARp110 promotes hepatocellular carcinoma progression via stabilization of CD24 mRNA

ADAR is highly expressed and correlated with poor prognosis in hepatocellular carcinoma (HCC), yet the role of its constitutive isoform ADARp110 in tumorigenesis remains elusive. We investigated the role of ADARp110 in HCC and underlying mechanisms using clinical samples, a hepatocyte-specific Adarp110 knock-in mouse model, and engineered cell lines. ADARp110 is overexpressed and associated with poor survival in both human and mouse HCC. It creates an immunosuppressive microenvironment by inhibiting total immune cells, particularly cytotoxic GZMB + CD8 + T cells infiltration, while augmenting Treg cells, MDSCs, and exhausted CD8 + T cells ratios. Mechanistically, ADARp110 interacts with SNRPD3 and RNPS1 to stabilize CD24 mRNA by inhibiting STAU1-mediated mRNA decay. CD24 protects HCC cells from two indispensable mechanisms: macrophage phagocytosis and oxidative stress. Genetic knockdown or monoclonal antibody treatment of CD24 inhibits ADARp110-overexpressing tumor growth. Our findings unveil different mechanisms for ADARp110 modulation of tumor immune microenvironment and identify CD24 as a promising therapeutic target for HCCs.

WeTICA: A directed search weighted ensemble based enhanced sampling method to estimate rare event kinetics in a reduced dimensional space

Estimating rare event kinetics from molecular dynamics simulations is a non-trivial task despite the great advances in enhanced sampling methods. Weighted Ensemble (WE) simulation, a special class of enhanced sampling techniques, offers a way to directly calculate kinetic rate constants from biased trajectories without the need to modify the underlying energy landscape using bias potentials. Conventional WE algorithms use different binning schemes to partition the collective variable (CV) space separating the two metastable states of interest. In this work, we have developed a new “binless” WE simulation algorithm to bypass the hurdles of optimizing binning procedures. Our proposed protocol (WeTICA) uses a low-dimensional CV space to drive the WE simulation toward the specified target state. We have applied this new algorithm to recover the unfolding kinetics of three proteins: (A) TC5b Trp-cage mutant, (B) TC10b Trp-cage mutant, and (C) Protein G, with unfolding times spanning the range between 3 and 40 μs using projections along predefined fixed Time-lagged Independent Component Analysis (TICA) eigenvectors as CVs. Calculated unfolding times converge to the reported values with good accuracy with more than one order of magnitude less cumulative WE simulation time than the unfolding time scales with or without a priori knowledge of the CVs that can capture unfolding. Our algorithm can be used with other linear CVs, not limited to TICA. Moreover, the new walker selection criteria for resampling employed in this algorithm can be used on more sophisticated nonlinear CV space for further improvements of binless WE methods.

Profile of Yosef Shiloh

<i>Ab initio</i> calculations of electric field gradients in H-bond rich molecular crystals with nearly experimental accuracy

Ab initio calculations of electric field gradients (EFGs) in molecular crystals have advanced significantly due to the gauge including projector augmented wave (GIPAW) formalism, which accounts for the infinite periodicity in crystals. However, theoretical accuracies still lag behind experimental ones, making it challenging to distinguish experimentally distinguishable similar structures, a deficiency largely attributed to the limitation of GIPAW codes to generalized gradient approximation (GGA) density functional theory (DFT) functionals. In this study, we investigate whether hybrid DFT functionals can enhance the EFG calculation accuracy and the associated geometry optimization. Using the many-body expansion method, we focus on nitrogen EFGs in amino acids with complex H-bonding, which are often poorly described with GGA functionals. Our results show that both functionals provide highly accurate calculations that surpass current studies and approach experimental precision. The accuracies are also almost three times higher than available GIPAW/GGA calculations in the literature. However, we show that this difference is not due to the GGA functional but rather due to the improper selection of the nitrogen quadrupole moment.