Heterogeneity and transcriptional drivers of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with limited treatment options. To characterize TNBC heterogeneity, we defined transcriptional, epigenetic, and metabolic subtypes and subtype-driving super-enhancers and transcription factors by combining functional and molecular profiling with computational analyses. Single-cell RNA sequencing revealed relative homogeneity of the major transcriptional subtypes (luminal, basal, and mesenchymal) within samples. We found that mesenchymal TNBCs share features with mesenchymal neuroblastoma and rhabdoid tumors and that the PRRX1 transcription factor is a key driver of these tumors. PRRX1 is sufficient for inducing mesenchymal features in basal but not in luminal TNBC cells via reprogramming super-enhancer landscapes, but it is not required for mesenchymal state maintenance or for cellular viability. Our comprehensive, large-scale, multiplatform, multiomics study of both experimental and clinical TNBC is an important resource for the scientific and clinical research communities and opens venues for future investigation.

Structure of the Schizosaccharomyces pombe Gtr-Lam complex reveals evolutionary divergence of mTORC1-dependent amino acid sensing.

mTORC1 is a protein kinase complex that controls cellular growth in response to nutrient availability. Amino acid signals are transmitted toward mTORC1 via the Rag/Gtr GTPases and their upstream regulators. An important regulator is LAMTOR, which localizes Rag/Gtr on the lysosomal/vacuole membrane. In human cells, LAMTOR consists of five subunits, but in yeast, only three or four. Currently, it is not known how variation of the subunit stoichiometry may affect its structural organization and biochemical properties. Here, we report a 3.1 Å-resolution structural model of the Gtr-Lam complex in Schizosaccharomyces pombe. We found that SpGtr shares conserved architecture as HsRag, but the intersubunit communication that coordinates nucleotide loading on the two subunits differs. In contrast, SpLam contains distinctive structural features, but its GTP-specific GEF activity toward SpGtr is evolutionarily conserved. Our results revealed unique evolutionary paths of the protein components of the mTORC1 pathway.

Redundant electrostatic interactions between GATOR1 and the Rag GTPase heterodimer drive efficient amino acid sensing in human cells.

Cells need to coordinate nutrient availability with their growth and proliferation. In eukaryotic cells, this coordination is mediated by the mechanistic target of the rapamycin complex 1 (mTORC1) pathway. mTORC1 activation is regulated by two GTPase units, the Rag GTPase heterodimer and the Rheb GTPase. The RagA-RagC heterodimer controls the subcellular localization of mTORC1, and its nucleotide loading states are strictly controlled by upstream regulators including amino acid sensors. A critical negative regulator of the Rag GTPase heterodimer is GATOR1. In the absence of amino acids, GATOR1 stimulates GTP hydrolysis by the RagA subunit to turn off mTORC1 signaling. Despite the enzymatic specificity of GATOR1 to RagA, a recent cryo-EM structural model of the human GATOR1-Rag-Ragulator complex reveals an unexpected interface between Depdc5, a subunit of GATOR1, and RagC. Currently, there is no functional characterization of this interface, nor do we know its biological relevance. Here, combining structure-function analysis, enzymatic kinetic measurements, and cell-based signaling assays, we identified a critical electrostatic interaction between Depdc5 and RagC. This interaction is mediated by the positively charged Arg-1407 residue on Depdc5 and a patch of negatively charged residues on the lateral side of RagC. Abrogating this interaction impairs the GAP activity of GATOR1 and cellular response to amino acid withdrawal. Our results reveal how GATOR1 coordinates the nucleotide loading states of the Rag GTPase heterodimer, and thus precisely controls cellular behavior in the absence of amino acids.

Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia.

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

Detergent modulates the conformational equilibrium of SARS-CoV-2 Spike during cryo-EM structural determination.

The Spike glycoprotein of SARS-CoV-2 mediates viral entry into the host cell via the interaction between its receptor binding domain (RBD) and human angiotensin-converting enzyme 2 (ACE2). Spike RBD has been reported to adopt two primary conformations, a closed conformation in which the binding site is shielded and unable to interact with ACE2, and an open conformation that is capable of binding ACE2. Many structural studies have probed the conformational space of the homotrimeric Spike from SARS-CoV-2. However, how sample buffer conditions used during structural determination influence the Spike conformation is currently unclear. Here, we systematically explored the impact of commonly used detergents on the conformational space of Spike. We show that in the presence of detergent, the Spike glycoprotein predominantly occupies a closed conformational state during cryo-EM structural determination. However, in the absence of detergent, such conformational compaction was neither observed by cryo-EM, nor by single-molecule FRET designed to visualize the movement of RBD in solution in real-time. Our results highlight the highly sensitive nature of the Spike conformational space to buffer composition during cryo-EM structural determination, and emphasize the importance of orthogonal biophysical approaches to validate the structural models obtained.

Protein interaction studies in human induced neurons indicate convergent biology underlying autism spectrum disorders.

Autism spectrum disorders (ASDs) have been linked to genes with enriched expression in the brain, but it is unclear how these genes converge into cell-type-specific networks. We built a protein-protein interaction network for 13 ASD-associated genes in human excitatory neurons derived from induced pluripotent stem cells (iPSCs). The network contains newly reported interactions and is enriched for genetic and transcriptional perturbations observed in individuals with ASDs. We leveraged the network data to show that the ASD-linked brain-specific isoform of ANK2 is important for its interactions with synaptic proteins and to characterize a PTEN-AKAP8L interaction that influences neuronal growth. The IGF2BP1-3 complex emerged as a convergent point in the network that may regulate a transcriptional circuit of ASD-associated genes. Our findings showcase cell-type-specific interactomes as a framework to complement genetic and transcriptomic data and illustrate how both individual and convergent interactions can lead to biological insights into ASDs.

JAK-STAT Signaling in Inflammatory Breast Cancer Enables Chemotherapy-Resistant Cell States.

Inflammatory breast cancer (IBC) is a difficult-to-treat disease with poor clinical outcomes due to high risk of metastasis and resistance to treatment. In breast cancer, CD44+CD24- cells possess stem cell-like features and contribute to disease progression, and we previously described a CD44+CD24-pSTAT3+ breast cancer cell subpopulation that is dependent on JAK2/STAT3 signaling. Here we report that CD44+CD24- cells are the most frequent cell type in IBC and are commonly pSTAT3+. Combination of JAK2/STAT3 inhibition with paclitaxel decreased IBC xenograft growth more than either agent alone. IBC cell lines resistant to paclitaxel and doxorubicin were developed and characterized to mimic therapeutic resistance in patients. Multi-omic profiling of parental and resistant cells revealed enrichment of genes associated with lineage identity and inflammation in chemotherapy-resistant derivatives. Integrated pSTAT3 chromatin immunoprecipitation sequencing and RNA sequencing (RNA-seq) analyses showed pSTAT3 regulates genes related to inflammation and epithelial-to-mesenchymal transition (EMT) in resistant cells, as well as PDE4A, a cAMP-specific phosphodiesterase. Metabolomic characterization identified elevated cAMP signaling and CREB as a candidate therapeutic target in IBC. Investigation of cellular dynamics and heterogeneity at the single cell level during chemotherapy and acquired resistance by CyTOF and single cell RNA-seq identified mechanisms of resistance including a shift from luminal to basal/mesenchymal cell states through selection for rare preexisting subpopulations or an acquired change. Finally, combination treatment with paclitaxel and JAK2/STAT3 inhibition prevented the emergence of the mesenchymal chemo-resistant subpopulation. These results provide mechanistic rational for combination of chemotherapy with inhibition of JAK2/STAT3 signaling as a more effective therapeutic strategy in IBC. SIGNIFICANCE: Chemotherapy resistance in inflammatory breast cancer is driven by the JAK2/STAT3 pathway, in part via cAMP/PKA signaling and a cell state switch, which can be overcome using paclitaxel combined with JAK2 inhibitors.

Cryo-EM structures of the human GATOR1-Rag-Ragulator complex reveal a spatial-constraint regulated GAP mechanism.

mTORC1 controls cellular metabolic processes in response to nutrient availability. Amino acid signals are transmitted to mTORC1 through the Rag GTPases, which are localized on the lysosomal surface by the Ragulator complex. The Rag GTPases receive amino acid signals from multiple upstream regulators. One negative regulator, GATOR1, is a GTPase activating protein (GAP) for RagA. GATOR1 binds to the Rag GTPases via two modes: an inhibitory mode and a GAP mode. How these two binding interactions coordinate to process amino acid signals is unknown. Here, we resolved three cryo-EM structural models of the GATOR1-Rag-Ragulator complex, with the Rag-Ragulator subcomplex occupying the inhibitory site, the GAP site, and both binding sites simultaneously. When the Rag GTPases bind to GATOR1 at the GAP site, both Rag subunits contact GATOR1 to coordinate their nucleotide loading states. These results reveal a potential GAP mechanism of GATOR1 during the mTORC1 inactivation process.

Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds.

While gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of "connectivity" to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics .

An interdomain hydrogen bond in the Rag GTPases maintains stable mTORC1 signaling in sensing amino acids.

Cellular growth and proliferation are primarily dictated by the mechanistic target of rapamycin complex 1 (mTORC1), which balances nutrient availability against the cell's anabolic needs. Central to the activity of mTORC1 is the RagA-RagC GTPase heterodimer, which under favorable conditions recruits the complex to the lysosomal surface to promote its activity. The RagA-RagC heterodimer has a unique architecture in that both subunits are active GTPases. To promote mTORC1 activity, the RagA subunit is loaded with GTP and the RagC subunit is loaded with GDP, while the opposite nucleotide-loading configuration inhibits this signaling pathway. Despite its unique molecular architecture, how the Rag GTPase heterodimer maintains the oppositely loaded nucleotide state remains elusive. Here, we applied structure-function analysis approach to the crystal structures of the Rag GTPase heterodimer and identified a key hydrogen bond that stabilizes the GDP-loaded state of the Rag GTPases. This hydrogen bond is mediated by the backbone carbonyl of Asn30 in the nucleotide-binding domain of RagA or Lys84 of RagC and the hydroxyl group on the side chain of Thr210 in the C-terminal roadblock domain of RagA or Ser266 of RagC, respectively. Eliminating this interdomain hydrogen bond abolishes the ability of the Rag GTPase to maintain its functional state, resulting in a distorted response to amino acid signals. Our results reveal that this long-distance interdomain interaction within the Rag GTPase is required for the maintenance and regulation of the mTORC1 nutrient-sensing pathway.

Structural and Functional Analysis of the D614G SARS-CoV-2 Spike Protein Variant.

The SARS-CoV-2 spike (S) protein variant D614G supplanted the ancestral virus worldwide, reaching near fixation in a matter of months. Here we show that D614G was more infectious than the ancestral form on human lung cells, colon cells, and on cells rendered permissive by ectopic expression of human ACE2 or of ACE2 orthologs from various mammals, including Chinese rufous horseshoe bat and Malayan pangolin. D614G did not alter S protein synthesis, processing, or incorporation into SARS-CoV-2 particles, but D614G affinity for ACE2 was reduced due to a faster dissociation rate. Assessment of the S protein trimer by cryo-electron microscopy showed that D614G disrupts an interprotomer contact and that the conformation is shifted toward an ACE2 binding-competent state, which is modeled to be on pathway for virion membrane fusion with target cells. Consistent with this more open conformation, neutralization potency of antibodies targeting the S protein receptor-binding domain was not attenuated.

Structural and Functional Analysis of the D614G SARS-CoV-2 Spike Protein Variant.

The SARS-CoV-2 spike (S) protein variant D614G supplanted the ancestral virus worldwide in a matter of months. Here we show that D614G was more infectious than the ancestral form on human lung cells, colon cells, and cells rendered permissive by ectopic expression of various mammalian ACE2 orthologs. Nonetheless, D614G affinity for ACE2 was reduced due to a faster dissociation rate. Assessment of the S protein trimer by cryo-electron microscopy showed that D614G disrupts a critical interprotomer contact and that this dramatically shifts the S protein trimer conformation toward an ACE2-binding and fusion-competent state. Consistent with the more open conformation, neutralization potency of antibodies targeting the S protein receptor-binding domain was not attenuated. These results indicate that D614G adopts conformations that make virion membrane fusion with the target cell membrane more probable but that D614G retains susceptibility to therapies that disrupt interaction of the SARS-CoV-2 S protein with the ACE2 receptor.

KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance.

Members of the KDM5 histone H3 lysine 4 demethylase family are associated with therapeutic resistance, including endocrine resistance in breast cancer, but the underlying mechanism is poorly defined. Here we show that genetic deletion of KDM5A/B or inhibition of KDM5 activity increases sensitivity to anti-estrogens by modulating estrogen receptor (ER) signaling and by decreasing cellular transcriptomic heterogeneity. Higher KDM5B expression levels are associated with higher transcriptomic heterogeneity and poor prognosis in ER+ breast tumors. Single-cell RNA sequencing, cellular barcoding, and mathematical modeling demonstrate that endocrine resistance is due to selection for pre-existing genetically distinct cells, while KDM5 inhibitor resistance is acquired. Our findings highlight the importance of cellular phenotypic heterogeneity in therapeutic resistance and identify KDM5A/B as key regulators of this process.

Characterization of aminoacyl-tRNA synthetase stability and substrate interaction by differential scanning fluorimetry.

Differential scanning fluorimetry (DSF) is a fluorescence-based assay to evaluate protein stability by determining protein melting temperatures. Here, we describe the application of DSF to investigate aminoacyl-tRNA synthetase (AARS) stability and interaction with ligands. Employing three bacterial AARS enzymes as model systems, methods are presented here for the use of DSF to measure the apparent temperatures at which AARSs undergo melting transitions, and the effect of AARS substrates and inhibitors. One important observation is that the extent of temperature stability realized by an AARS in response to a particular bound ligand cannot be predicted a priori. The DSF method thus serves as a rapid and highly quantitative approach to measure AARS stability, and the ability of ligands to influence the temperature at which unfolding transitions occur.

Optimum Exchange for Calculation of Excitation Energies and Hyperpolarizabilities of Organic Electro-optic Chromophores.

Organic electro-optic (OEO) materials integrated into silicon-organic hybrid devices afford significant improvements in size, weight, power, and bandwidth performance of integrated electronic/photonic systems critical for current and next generation telecommunication, computer, sensor, transportation, and defense technologies. Improvement in molecular first hyperpolarizability (ß), and in turn electro-optic activity, is crucial to optimizing device performance. Common hybrid density functional theory (DFT) methods, while attractive due to their computational scaling, often perform poorly for optical properties in systems with substantial intramolecular charge-transfer character, such as OEO chromophores. This study evaluates the utility of the long-range corrected (LC) DFT methods for computation of the molecular second-order nonlinear optical response. We compare calculated results for a 14-molecule benchmark set of OEO chromophores with the corresponding experimentally measured ß and one-photon absorption energy, λmax. We analyze the distance dependence of the fraction of exact exchange in LC-DFT methods for accurately computing these properties for OEO chromophores. We also examine systematic tuning of the range-separation parameter to enforce Koopmans'/ionization potential theorem. This tuning method improves prediction of excitation energies but is not reliable for predicting the hyperpolarizabilities of larger chromophores since the tuning parameter value can be too small, leading to instabilities in the computation of ßHRS. Additionally, we find that the size dependence of the optimal tuning parameter for the ionization potential has the opposite size dependence of optimal tuning parameter for best agreement with the experimental λmax, suggesting the tuning for the ionization potential is unreliable for extended conjugated systems.