Role of Helicity in DNA Hairpin Folding Dynamics.

We study hairpin folding dynamics by means of extensive molecular dynamics simulations, with particular attention paid to the influence of helicity on the folding time. We find that the dynamical exponent α in the anomalous scaling n(t)∼t^{1/α} of the hairpin length n with time changes from 1.6 (≃1+ν, where ν is the Flory exponent) to 1.2 (≃2ν) in three dimensions, when duplex helicity is removed. The relation α=2ν in rotationless hairpin folding is further verified in two dimensions (ν=0.75) and for a ghost chain (ν=0.5). Our findings suggest that the folding dynamics in long helical chains is governed by the duplex dynamics, contrasting the earlier understanding based on the stem-flower picture of unpaired segments. We propose a scaling argument for α=1+ν in helical chains, assuming that duplex relaxation required for orientational positioning of the next pair of bases is the rate-limiting process.

Coherent organization in gene regulation: a study on six networks.

Structural and dynamical fingerprints of evolutionary optimization in biological networks are still unclear. Here we analyze the dynamics of genetic regulatory networks responsible for the regulation of cell cycle and cell differentiation in three organisms or cell types each, and show that they follow a version of Hebb's rule which we have termed coherence. More precisely, we find that simultaneously expressed genes with a common target are less likely to act antagonistically at the attractors of the regulatory dynamics. We then investigate the dependence of coherence on structural parameters, such as the mean number of inputs per node and the activatory/repressory interaction ratio, as well as on dynamically determined quantities, such as the basin size and the number of expressed genes.

Genome-wide target analysis of NEUROD2 provides new insights into regulation of cortical projection neuron migration and differentiation.

BACKGROUND: Cellular differentiation programs are controlled, to a large extent, by the combinatorial functioning of specific transcription factors. Cortical projection neurons constitute the major excitatory neuron population within the cortex and mediate long distance communication between the cortex and other brain regions. Our understanding of effector transcription factors and their downstream transcriptional programs that direct the differentiation process of cortical projection neurons is far from complete. RESULTS: In this study, we carried out a ChIP-Seq (chromatin-immunoprecipitation and sequencing) analysis of NEUROD2, an effector transcription factor expressed in lineages of cortical projection neurons during the peak of cortical excitatory neurogenesis. Our results suggest that during cortical development NEUROD2 targets key genes that are required for Reelin signaling, a major pathway that regulates the migration of neurons from germinal zones to their final layers of residence within the cortex. We also find that NEUROD2 binds to a large set of genes with functions in layer-specific differentiation and in axonal pathfinding of cortical projection neurons. CONCLUSIONS: Our analysis of in vivo NEUROD2 target genes offers mechanistic insight into signaling pathways that regulate neuronal migration and axon guidance and identifies genes that are likely to be required for proper cortical development.

Coherent regulation in yeast's cell-cycle network.

We define a measure of coherent activity for gene regulatory networks, a property that reflects the unity of purpose between the regulatory agents with a common target. We propose that such harmonious regulatory action is desirable under a demand for energy efficiency and may be selected for under evolutionary pressures. We consider two recent models of the cell-cycle regulatory network of the yeast, Saccharomyces cerevisiae as a case study and calculate their degree of coherence. A comparison with random networks of similar size and composition reveals that the yeast's cell-cycle regulation is wired to yield an exceptionally high level of coherent regulatory activity. We also investigate the mean degree of coherence as a function of the network size, connectivity and the fraction of repressory/activatory interactions.

Mode coupling points to functionally important residues in myosin II.

Relevance of mode coupling to energy/information transfer during protein function, particularly in the context of allosteric interactions is widely accepted. However, existing evidence in favor of this hypothesis comes essentially from model systems. We here report a novel formal analysis of the near-native dynamics of myosin II, which allows us to explore the impact of the interaction between possibly non-Gaussian vibrational modes on fluctutational dynamics. We show that an information-theoretic measure based on mode coupling alone yields a ranking of residues with a statistically significant bias favoring the functionally critical locations identified by experiments on myosin II.

Siamese Graph Convolutional Network quantifies increasing structure-function discrepancy over the cognitive decline continuum.

BACKGROUND AND OBJECTIVE: Alzheimer's disease dementia (ADD) is well known to induce alterations in both structural and functional brain connectivity. However, reported changes in connectivity are mostly limited to global/local network features, which have poor specificity for diagnostic purposes. Following recent advances in machine learning, deep neural networks, particularly Graph Neural Network (GNN) based approaches, have found applications in brain research as well. The majority of existing applications of GNNs employ a single network (uni-modal or structure/function unified), despite the widely accepted view that there is a nontrivial interdependence between the brain's structural connectivity and the neural activity patterns, which is hypothesized to be disrupted in ADD. This disruption is quantified as a discrepancy score by the proposed "structure-function discrepancy learning network" (sfDLN) and its distribution is studied over the spectrum of clinical cognitive decline. The measured discrepancy score is utilized as a diagnostic biomarker and is compared with state-of-the-art diagnostic classifiers. METHODS: sfDLN is a GNN with a siamese architecture built on the hypothesis that the mismatch between structural and functional connectivity patterns increases over the cognitive decline spectrum, starting from subjective cognitive impairment (SCI), passing through a mid-stage mild cognitive impairment (MCI), and ending up with ADD. The structural brain connectome (sNET) built using diffusion MRI-based tractography and the novel, sparse (lean) functional brain connectome (ℓNET) built using fMRI are input to sfDLN. The siamese sfDLN is trained to extract connectome representations and a discrepancy (dissimilarity) score that complies with the proposed hypothesis and is blindly tested on an MCI group. RESULTS: The sfDLN generated structure-function discrepancy scores show high disparity between ADD and SCI subjects. Leave-one-out experiments of SCI-ADD classification over a cohort of 42 subjects reach 88% accuracy, surpassing state-of-the-art GNN-based classifiers in the literature. Furthermore, a blind assessment over a cohort of 46 MCI subjects confirmed that it captures the intermediary character of the MCI group. GNNExplainer module employed to investigate the anatomical determinants of the observed discrepancy confirms that sfDLN attends to cortical regions neurologically relevant to ADD. CONCLUSION: In support of our hypothesis, the harmony between the structural and functional organization of the brain degrades with increasing cognitive decline. This discrepancy, shown to be rooted in brain regions neurologically relevant to ADD, can be quantified by sfDLN and outperforms state-of-the-art GNN-based ADD classification methods when used as a biomarker.

Terminal neuron localization to the upper cortical plate is controlled by the transcription factor NEUROD2.

Excitatory neurons of the mammalian cerebral cortex are organized into six functional layers characterized by unique patterns of connectivity, as well as distinctive physiological and morphological properties. Cortical layers appear after a highly regulated migration process in which cells move from the deeper, proliferative zone toward the superficial layers. Importantly, defects in this radial migration process have been implicated in neurodevelopmental and psychiatric diseases. Here we report that during the final stages of migration, transcription factor Neurogenic Differentiation 2 (Neurod2) contributes to terminal cellular localization within the cortical plate. In mice, in utero knockdown of Neurod2 resulted in reduced numbers of neurons localized to the uppermost region of the developing cortex, also termed the primitive cortical zone. Our ChIP-Seq and RNA-Seq analyses of genes regulated by NEUROD2 in the developing cortex identified a number of key target genes with known roles in Reelin signaling, a critical regulator of neuronal migration. Our focused analysis of regulation of the Reln gene, encoding the extracellular ligand REELIN, uncovered NEUROD2 binding to conserved E-box elements in multiple introns. Furthermore, we demonstrate that knockdown of NEUROD2 in primary cortical neurons resulted in a strong increase in Reln gene expression at the mRNA level, as well as a slight upregulation at the protein level. These data reveal a new role for NEUROD2 during the late stages of neuronal migration, and our analysis of its genomic targets offers new genes with potential roles in cortical lamination.

NEUROD2 Regulates Stim1 Expression and Store-Operated Calcium Entry in Cortical Neurons.

Calcium signaling controls many key processes in neurons, including gene expression, axon guidance, and synaptic plasticity. In contrast to calcium influx through voltage- or neurotransmitter-gated channels, regulatory pathways that control store-operated calcium entry (SOCE) in neurons are poorly understood. Here, we report a transcriptional control of Stim1 (stromal interaction molecule 1) gene, which is a major sensor of endoplasmic reticulum (ER) calcium levels and a regulator of SOCE. By using a genome-wide chromatin immunoprecipitation and sequencing approach in mice, we find that NEUROD2, a neurogenic transcription factor, binds to an intronic element within the Stim1 gene. We show that NEUROD2 limits Stim1 expression in cortical neurons and consequently fine-tunes the SOCE response upon depletion of ER calcium. Our findings reveal a novel mechanism that regulates neuronal calcium homeostasis during cortical development.

Denaturation of circular DNA: supercoils and overtwist.

The denaturation transition of circular DNA is studied within a Poland-Scheraga-type approach, generalized to account for the fact that the total linking number (LK), which measures the number of windings of one strand around the other, is conserved. In the model the LK conservation is maintained by invoking both overtwisting and writhing (supercoiling) mechanisms. This generalizes previous studies, which considered each mechanism separately. The phase diagram of the model is analyzed as a function of the temperature and the elastic constant κ associated with the overtwisting energy for any given loop entropy exponent c. As in the case where the two mechanisms apply separately, the model exhibits no denaturation transition for c ≤ 2. For c > 2 and κ = 0 we find that the model exhibits a first-order transition. The transition becomes of higher order for any κ > 0. We also calculate the contribution of the two mechanisms separately in maintaining the conservation of the linking number and find that it is weakly dependent on the loop exponent c.

Macroscopic loop formation in circular DNA denaturation.

The statistical mechanics of DNA denaturation under fixed linking number is qualitatively different from that of unconstrained DNA. Quantitatively different melting scenarios are reached from two alternative assumptions, namely, that the denatured loops are formed at the expense of (i) overtwist or (ii) supercoils. Recent work has shown that the supercoiling mechanism results in a picture similar to Bose-Einstein condensation where a macroscopic loop appears at T{c} and grows steadily with temperature, while the nature of the denatured phase for the overtwisting case has not been studied. By extending an earlier result, we show here that a macroscopic loop appears in the overtwisting scenario as well. We calculate its size as a function of temperature and show that the fraction of the total sum of microscopic loops decreases above T{c}, with a cusp at the critical point.

Deep spin-glass hysteresis-area collapse and scaling in the three-dimensional ±J Ising model.

We investigate the dissipative loss in the ±J Ising spin glass in three dimensions through the scaling of the hysteresis area, for a maximum magnetic field that is equal to the saturation field. We perform a systematic analysis for the whole range of the bond randomness as a function of the sweep rate by means of frustration-preserving hard-spin mean-field theory. Data collapse within the entirety of the spin-glass phase driven adiabatically (i.e., infinitely slow field variation) is found, revealing a power-law scaling of the hysteresis area as a function of the antiferromagnetic bond fraction and the temperature. Two dynamic regimes separated by a threshold frequency ω(c) characterize the dependence on the sweep rate of the oscillating field. For ω<ω(c), the hysteresis area is equal to its value in the adiabatic limit ω=0, while for ω>ω(c) it increases with the frequency through another randomness-dependent power law.

Twist-writhe partitioning in a coarse-grained DNA minicircle model.

Here we present a systematic study of supercoil formation in DNA minicircles under varying linking number by using molecular-dynamics simulations of a two-bead coarse-grained model. Our model is designed with the purpose of simulating long chains without sacrificing the characteristic structural properties of the DNA molecule, such as its helicity, backbone directionality, and the presence of major and minor grooves. The model parameters are extracted directly from full-atomistic simulations of DNA oligomers via Boltzmann inversion; therefore, our results can be interpreted as an extrapolation of those simulations to presently inaccessible chain lengths and simulation times. Using this model, we measure the twist/writhe partitioning in DNA minicircles, in particular its dependence on the chain length and excess linking number. We observe an asymmetric supercoiling transition consistent with experiments. Our results suggest that the fraction of the linking number absorbed as twist and writhe is nontrivially dependent on chain length and excess linking number. Beyond the supercoiling transition, chains of the order of one persistence length carry equal amounts of twist and writhe. For longer chains, an increasing fraction of the linking number is absorbed by the writhe.

The information coded in the yeast response elements accounts for most of the topological properties of its transcriptional regulation network.

The regulation of gene expression in a cell relies to a major extent on transcription factors, proteins which recognize and bind the DNA at specific binding sites (response elements) within promoter regions associated with each gene. We present an information theoretic approach to modeling transcriptional regulatory networks, in terms of a simple "sequence-matching" rule and the statistics of the occurrence of binding sequences of given specificity in random promoter regions. The crucial biological input is the distribution of the amount of information coded in these cognate response elements and the length distribution of the promoter regions. We provide an analysis of the transcriptional regulatory network of yeast Saccharomyces cerevisiae, which we extract from the available databases, with respect to the degree distributions, clustering coefficient, degree correlations, rich-club coefficient and the k-core structure. We find that these topological features are in remarkable agreement with those predicted by our model, on the basis of the amount of information coded in the interaction between the transcription factors and response elements.