Hydrogels are commonly used as artificial extracellular matrices for 3D cell culture and for tissue engineering. Viscoelastic hydrogels with tunable stress relaxation have recently been developed, and stress relaxation in the hydrogels has been found to play a key role in regulating cell behaviors such as differentiation, spreading, and proliferation. Here we report a simple but precise materials approach to tuning stress relaxation of alginate hydrogels with polyethylene glycol (PEG) covalently grafted onto the alginate. Hydrogel relaxation was modulated independent of the initial elastic modulus by varying molecular weight and concentration of PEG along with calcium crosslinking of the alginate. Increased concentration and molecular weight of the PEG resulted in faster stress relaxation, a higher loss modulus, and increased creep. Interestingly, we found that stress relaxation of the hydrogels is determined by the total mass amount of PEG in the hydrogel, and not the molecular weight or concentration of PEG chains alone. We then evaluated the utility of these hydrogels for 3D cell culture. Faster relaxation in RGD-coupled alginate-PEG hydrogels led to increased spreading and proliferation of fibroblasts, and enhanced osteogenic differentiation of mesenchymal stem cells (MSCs). Thus, this work establishes a new materials approach to tuning stress relaxation in alginate hydrogels for 3D cell culture.

The ability to selectively activate function of particular proteins via pharmacological agents is a longstanding goal in chemical biology. Recently, we reported an approach for designing a de novo allosteric effector site directly into the catalytic domain of an enzyme. This approach is distinct from traditional chemical rescue of enzymes in that it relies on disruption and restoration of structure, rather than active site chemistry, as a means to achieve modulate function. However, rationally identifying analogous de novo binding sites in other enzymes represents a key challenge for extending this approach to introduce allosteric control into other enzymes. Here we show that mutation sites leading to protein inactivation via tryptophan-to-glycine substitution and allowing (partial) reactivation by the subsequent addition of indole are remarkably frequent. Through a suite of methods including a cell-based reporter assay, computational structure prediction and energetic analysis, fluorescence studies, enzymology, pulse proteolysis, X-ray crystallography, and hydrogen-deuterium mass spectrometry, we find that these switchable proteins are most commonly modulated indirectly, through control of protein stability. Addition of indole in these cases rescues activity not by reverting a discrete conformational change, as we had observed in the sole previously reported example, but rather rescues activity by restoring protein stability. This important finding will dramatically impact the design of future switches and sensors built by this approach, since evaluating stability differences associated with cavity-forming mutations is a far more tractable task than predicting allosteric conformational changes. By analogy to natural signaling systems, the insights from this study further raise the exciting prospect of modulating stability to design optimal recognition properties into future de novo switches and sensors built through chemical rescue of structure.

We aimed to elucidate the characteristics of HIV molecular epidemiology and identify transmission hubs in eastern China using genetic transmission network and lineage analyses. HIV-TRACE was used to infer putative relationships. Across the range of epidemiologically-plausible genetic distance (GD) thresholds (0.1-2.0%), a sensitivity analysis was performed to determine the optimal threshold, generating the maximum number of transmission clusters and providing reliable resolution without merging different small clusters into a single large cluster. Characteristics of genetically linked individuals were analyzed using logistic regression. Assortativity (shared characteristics) analysis was performed to infer shared attributes between putative partners. 1,993 persons living with HIV-1 were enrolled. The determined GD thresholds within subtypes CRF07_BC, CRF01_AE, and B were 0.5%, 1.2%, and 1.7%, respectively, and 826 of 1,993 (41.4%) sequences were linked with at least one other sequence, forming 188 transmission clusters of 2-80 sequences. Clustering rates for the main subtypes CRF01_AE, CRF07_BC, and B were 50.9% (523/1027), 34.2% (256/749), and 32.1% (25/78), respectively. Median cluster sizes of these subtypes were 2 (2-52, n = 523), 2 (2-80, n = 256), and 3 (2-6, n = 25), respectively. Subtypes in individuals diagnosed and residing in Hangzhou city (OR = 1.423, 95% CI: 1.168-1.734) and men who have sex with men (MSM) were more likely to cluster. Assortativity analysis revealed individuals were more likely to be genetically linked to individuals from the same age group (AIage = 0.090, P<0.001) and the same area of residency in Zhejiang (AIcity = 0.078, P<0.001). Additionally, students living with HIV were more likely to be linked with students than show a random distribution (AI student = 0.740, P<0.01). These results highlight the importance of Hangzhou City in the regional epidemic and show that MSM comprise the population rapidly transmitting HIV in Zhejiang Province. We also provide a molecular epidemiology framework for improving our understanding of HIV transmission dynamics in eastern China.

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

Low-grade, sustained inflammation in white adipose tissue (WAT) characterizes obesity and coincides with type 2 diabetes mellitus (T2DM). However, pharmacological targeting of inflammation lacks durable therapeutic effects in insulin-resistant conditions. Through a computational screen, we discovered that the FDA-approved rheumatoid arthritis drug auranofin improved insulin sensitivity and normalized obesity-associated abnormalities, including hepatic steatosis and hyperinsulinemia in mouse models of T2DM. We also discovered that auranofin accumulation in WAT depleted inflammatory responses to a high-fat diet without altering body composition in obese wild-type mice. Surprisingly, elevated leptin levels and blunted beta-adrenergic receptor activity achieved by leptin receptor deletion abolished the antidiabetic effects of auranofin. These experiments also revealed that the metabolic benefits of leptin reduction were superior to immune impacts of auranofin in WAT. Our studies uncover important metabolic properties of anti-inflammatory treatments and contribute to the notion that leptin reduction in the periphery can be accomplished to treat obesity and T2DM.

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.

Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. Coexpression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response modules defining previously unidentified neural-immune mechanisms. We integrated genetic and genomic data to perform a transcriptome-wide association study, prioritizing disease loci likely mediated by cis effects on brain expression. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.

Genes implicated in neuropsychiatric disorders are active in human fetal brain, yet difficult to study in a longitudinal fashion. We demonstrate that organoids from human pluripotent cells model cerebral cortical development on the molecular level before 16 weeks postconception. A multiomics analysis revealed differentially active genes and enhancers, with the greatest changes occurring at the transition from stem cells to progenitors. Networks of converging gene and enhancer modules were assembled into six and four global patterns of expression and activity across time. A pattern with progressive down-regulation was enriched with human-gained enhancers, suggesting their importance in early human brain development. A few convergent gene and enhancer modules were enriched in autism-associated genes and genomic variants in autistic children. The organoid model helps identify functional elements that may drive disease onset.