Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD.

Neuropsychiatric disorders classically lack defining brain pathologies, but recent work has demonstrated dysregulation at the molecular level, characterized by transcriptomic and epigenetic alterations1-3. In autism spectrum disorder (ASD), this molecular pathology involves the upregulation of microglial, astrocyte and neural-immune genes, the downregulation of synaptic genes, and attenuation of gene-expression gradients in cortex1,2,4-6. However, whether these changes are limited to cortical association regions or are more widespread remains unknown. To address this issue, we performed RNA-sequencing analysis of 725 brain samples spanning 11 cortical areas from 112 post-mortem samples from individuals with ASD and neurotypical controls. We find widespread transcriptomic changes across the cortex in ASD, exhibiting an anterior-to-posterior gradient, with the greatest differences in primary visual cortex, coincident with an attenuation of the typical transcriptomic differences between cortical regions. Single-nucleus RNA-sequencing and methylation profiling demonstrate that this robust molecular signature reflects changes in cell-type-specific gene expression, particularly affecting excitatory neurons and glia. Both rare and common ASD-associated genetic variation converge within a downregulated co-expression module involving synaptic signalling, and common variation alone is enriched within a module of upregulated protein chaperone genes. These results highlight widespread molecular changes across the cerebral cortex in ASD, extending beyond association cortex to broadly involve primary sensory regions.

Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases.

Single nucleotide polymorphisms (SNPs) from omics data create a reidentification risk for individuals and their relatives. Although the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly shown, the availability of small sets of noisy genotypes, from environmental DNA samples or functional genomics data, motivated us to quantify their informativeness. We present a computational tool suite, termed Privacy Leakage by Inference across Genotypic HMM Trajectories (PLIGHT), using population-genetics-based hidden Markov models (HMMs) of recombination and mutation to find piecewise alignment of small, noisy SNP sets to reference haplotype databases. We explore cases in which query individuals are either known to be in the database, or not, and consider several genotype queries, including those from environmental sample swabs from known individuals and from simulated "mosaics" (two-individual composites). Using PLIGHT on a database with ∼5000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, ∼20 can identify both components in two-individual mosaics, and 20-30 can identify first-order relatives. Using noisy environmental-sample-derived SNPs, PLIGHT identifies individuals in a database using ∼30 SNPs. Even when the individuals are not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained SNP imputation. Finally, by quantifying privacy leakage from sparse SNP sets, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about population membership or allele frequency. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from genomic data.

Insights from incorporating quantum computing into drug design workflows.

MOTIVATION: While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS: We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION: Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A REDOR ssNMR Investigation of the Role of an N-Terminus Lysine in R5 Silica Recognition.

Diatoms are unicellular algae that construct cell walls called frustules by the precipitation of silica, using special proteins that order the silica into a wide variety of nanostructures. The diatom species Cylindrotheca fusiformis contains proteins called silaffins within its frustules, which are believed to assemble into supramolecular matrices that serve as both accelerators and templates for silica deposition. Studying the properties of these biosilicification proteins has allowed the design of new protein and peptide systems that generate customizable silica nanostructures, with potential generalization to other mineral systems. It is essential to understand the mechanisms of aggregation of the protein and its coprecipitation with silica. We continue previous investigations into the peptide R5, derived from silaffin protein sil1p, shown to independently catalyze the precipitation of silica nanospheres in vitro. We used the solid-state NMR technique 13C{29Si} and 15N{29Si} REDOR to investigate the structure and interactions of R5 in complex with coprecipitated silica. These experiments are sensitive to the strength of magnetic dipole-dipole interactions between the 13C nuclei in R5 and the 29Si nuclei in the silica and thus yield distance between parts of R5 and 29Si in silica. Our data show strong interactions and short internuclear distances of 3.74 ± 0.20 Å between 13C═O Lys3 and silica. On the other hand, the Cα and Cß nuclei show little or no interaction with 29Si. This selective proximity between the K3 C═O and the silica supports a previously proposed mechanism of rapid silicification of the antimicrobial peptide KSL (KKVVFKVKFK) through an imidate intermediate. This study reports for the first time a direct interaction between the N-terminus of R5 and silica, leading us to believe that the N-terminus of R5 is a key component in the molecular recognition process and a major factor in silica morphogenesis.

A study of phenylalanine side-chain dynamics in surface-adsorbed peptides using solid-state deuterium NMR and rotamer library statistics.

Extracellular matrix proteins adsorbed onto mineral surfaces exist in a unique environment where the structure and dynamics of the protein can be altered profoundly. To further elucidate how the mineral surface impacts molecular properties, we perform a comparative study of the dynamics of nonpolar side chains within the mineral-recognition domain of the biomineralization protein salivary statherin adsorbed onto its native hydroxyapatite (HAP) mineral surface versus the dynamics displayed by the native protein in the hydrated solid state. Specifically, the dynamics of phenylalanine side chains (viz., F7 and F14) located in the surface-adsorbed 15-amino acid HAP-recognition fragment (SN15: DpSpSEEKFLRRIGRFG) are studied using deuterium magic angle spinning ((2)H MAS) line shape and spin-lattice relaxation measurements. (2)H NMR MAS spectra and T1 relaxation times obtained from the deuterated phenylalanine side chains in free and HAP-adsorbed SN15 are fitted to models where the side chains are assumed to exchange between rotameric states and where the exchange rates and a priori rotameric state populations are varied iteratively. In condensed proteins, phenylalanine side-chain dynamics are dominated by 180° flips of the phenyl ring, i.e., the "π flip". However, for both F7 and F14, the number of exchanging side-chain rotameric states increases in the HAP-bound complex relative to the unbound solid sample, indicating that increased dynamic freedom accompanies introduction of the protein into the biofilm state. The observed rotameric exchange dynamics in the HAP-bound complex are on the order of 5-6 × 10(6) s(-1), as determined from the deuterium MAS line shapes. The dynamics in the HAP-bound complex are also shown to have some solution-like behavioral characteristics, with some interesting deviations from rotameric library statistics.

Silica morphogenesis by lysine-leucine peptides with hydrophobic periodicity.

The use of biomimetic approaches in the production of inorganic nanostructures is of great interest to the scientific and industrial community due to the relatively moderate physical conditions needed. In this vein, taking cues from silaffin proteins used by unicellular diatoms, several studies have identified peptide candidates for the production of silica nanostructures. In the current article, we study intensively one such silica-precipitating peptide, LKα14 (Ac-LKKLLKLLKKLLKL-c), an amphiphilic lysine/leucine repeat peptide that self-organizes into an α-helical secondary structure under appropriate concentration and buffer conditions. The suggested mechanism of precipitation is that the sequestration of hydrophilic lysines on one side of this helix allows interaction with the negatively charged surface of silica nanoparticles, which in turn can aggregate further into larger structures. To investigate the process, we carry out 1D and 2D solid-state NMR (ssNMR) studies on samples with one or two uniformly (13)C- and (15)N-labeled residues to determine the backbone and side-chain chemical shifts. We also further study the dynamics of two leucine residues in the sequence through (13)C spin-lattice relaxation times (T1) to determine the impact of silica coprecipitation on their mobility. Our results confirm the α-helical secondary structure in both the neat and silica-complexed states of the peptide, and the patterns of chemical shift and relaxation time changes between the two states suggest possible mechanisms of self-aggregation and silica precipitation.

Single-cell genomics and regulatory networks for 388 human brains.

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Single-cell genomics and regulatory networks for 388 human brains.

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

The Cellular Underpinnings of the Human Cortical Connectome.

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cortex. The cortical sheet can be broadly divided into distinct networks, which are further embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here, using transcriptional data from the Allen Human Brain Atlas, we demonstrate that imputed cell type distributions are spatially coupled to the functional organization of cortex, as estimated through fMRI. Cortical cellular profiles follow the macro-scale organization of the functional gradients as well as the associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on post-mortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

Theory of nonrigid rotational motion applied to NMR relaxation in RNA.

Solution NMR spectroscopy can elucidate many features of the structure and dynamics of macromolecules, yet relaxation measurements, the most common source of experimental information on dynamics, can sample only certain ranges of dynamic rates. A complete characterization of motion of a macromolecule thus requires the introduction of complementary experimental approaches. Solid-state NMR spectroscopy successfully probes the time scale of nanoseconds to microseconds, a dynamic window where solution NMR results have been deficient, and probes conditions where the averaging effects of rotational diffusion of the molecule are absent. Combining the results of the two distinct techniques within a single framework provides greater insight into dynamics, but this task requires the common interpretation of results recorded under very different experimental conditions. Herein, we provide a unified description of dynamics that is robust to the presence of large-scale conformational exchange, where the diffusion tensor of the molecule varies on a time scale comparable to rotational diffusion in solution. We apply this methodology to the HIV-1 TAR RNA molecule, where conformational rearrangements are both substantial and functionally important. The formalism described herein is of greater generality than earlier combined solid-state/solution NMR interpretations, if detailed molecular structures are available, and can offer a more complete description of RNA dynamics than either solution or solid-state NMR spectroscopy alone.

Combining Molecular and Spin Dynamics Simulations with Solid-State NMR: A Case Study of Amphiphilic Lysine-Leucine Repeat Peptide Aggregates.

Interpreting dynamics in solid-state molecular systems requires characterization of the potentially heterogeneous environmental contexts of molecules. In particular, the analysis of solid-state nuclear magnetic resonance (ssNMR) data to elucidate molecular dynamics (MD) involves modeling the restriction to overall tumbling by neighbors, as well as the concentrations of water and buffer. In this exploration of the factors that influence motion, we utilize atomistic MD trajectories of peptide aggregates with varying hydration to mimic an amorphous solid-state environment and predict ssNMR relaxation rates. We also account for spin diffusion in multiply spin-labeled (up to 19 nuclei) residues, with several models of dipolar-coupling networks. The framework serves as a general approach to determine essential spin couplings affecting relaxation, benchmark MD force fields, and reveal the hydration dependence of dynamics in a crowded environment. We demonstrate the methodology on a previously characterized amphiphilic 14-residue lysine-leucine repeat peptide, LKα14 (Ac-LKKLLKLLKKLLKL-c), which has an α-helical secondary structure and putatively forms leucine-burying tetramers in the solid state. We measure the R1 relaxation rates of uniformly 13C-labeled and site-specific 2H-labeled leucines in the hydrophobic core of LKα14 at multiple hydration levels. Studies of 9 and 18 tetramer bundles reveal the following: (a) for the incoherent component of 13C relaxation, the nearest-neighbor spin interactions dominate, while the 1H-1H interactions have minimal impact; (b) the AMBER ff14SB dihedral barriers for the leucine Cγ-Cδ bond ("methyl rotation barriers") must be lowered by a factor of 0.7 to better match the 2H data; (c) proton-driven spin diffusion explains some of the discrepancy between experimental and simulated rates for the Cß and Cα nuclei; and (d) 13C relaxation rates are mostly underestimated in the MD simulations at all hydrations, and the discrepancies identify likely motions missing in the 50 ns MD trajectories.

Ultraslow Domain Motions in HIV-1 TAR RNA Revealed by Solid-State Deuterium NMR.

Intrinsic motions may allow HIV-1 transactivation response (TAR) RNA to change its conformation to form a functional complex with the Tat protein, which is essential for viral replication. Understanding the dynamic properties of TAR necessitates determining motion on the intermediate nanosecond-to-microsecond time scale. To this end, we performed solid-state deuterium NMR line-shape and T1Z relaxation-time experiments to measure intermediate motions for two uridine residues, U40 and U42, within the lower helix of TAR. We infer global motions at rates of ∼105 s-1 in the lower helix, which are much slower than those in the upper helix (∼106 s-1), indicating that the two helical domains reorient independently of one another in the solid-state sample. These results contribute to the aim of fully describing the properties of functional motions in TAR RNA.

Elucidating molecular motion through structural and dynamic filters of energy-minimized conformer ensembles.

Complex RNA structures are constructed from helical segments connected by flexible loops that move spontaneously and in response to binding of small molecule ligands and proteins. Understanding the conformational variability of RNA requires the characterization of the coupled time evolution of interconnected flexible domains. To elucidate the collective molecular motions and explore the conformational landscape of the HIV-1 TAR RNA, we describe a new methodology that utilizes energy-minimized structures generated by the program "Fragment Assembly of RNA with Full-Atom Refinement (FARFAR)". We apply structural filters in the form of experimental residual dipolar couplings (RDCs) to select a subset of discrete energy-minimized conformers and carry out principal component analyses (PCA) to corroborate the choice of the filtered subset. We use this subset of structures to calculate solution T1 and T(1ρ) relaxation times for (13)C spins in multiple residues in different domains of the molecule using two simulation protocols that we previously published. We match the experimental T1 times to within 2% and the T(1ρ) times to within less than 10% for helical residues. These results introduce a protocol to construct viable dynamic trajectories for RNA molecules that accord well with experimental NMR data and support the notion that the motions of the helical portions of this small RNA can be described by a relatively small number of discrete conformations exchanging over time scales longer than 1 µs.

Slow exchange model of nonrigid rotational motion in RNA for combined solid-state and solution NMR studies.

Functional RNA molecules are conformationally dynamic and sample a multitude of dynamic modes over a wide range of frequencies. Thus, a comprehensive description of RNA dynamics requires the inclusion of a broad range of motions across multiple dynamic rates which must be derived from multiple spectroscopies. Here we describe a slow conformational exchange theoretical approach to combining the description of local motions in RNA that occur in the nanosecond to microsecond window and are detected by solid-state NMR with nonrigid rotational motion of the HIV-1 transactivation response element (TAR) RNA in solution as observed by solution NMR. This theoretical model unifies the experimental results generated by solution and solid-state NMR and provides a comprehensive view of the dynamics of HIV-1 TAR RNA, a well-known paradigm of an RNA where function requires extensive conformational rearrangements. This methodology provides a quantitative atomic level view of the amplitudes and rates of the local and collective displacements of the TAR RNA molecule and provides directly motional parameters for the conformational capture hypothesis of this classical RNA-ligand interaction.