Substrate binding and inhibition of the anion exchanger 1 transporter.

Anion exchanger 1 (AE1), a member of the solute carrier (SLC) family, is the primary bicarbonate transporter in erythrocytes, regulating pH levels and CO2 transport between lungs and tissues. Previous studies characterized its role in erythrocyte structure and provided insight into transport regulation. However, key questions remain regarding substrate binding and transport, mechanisms of drug inhibition and modulation by membrane components. Here we present seven cryo-EM structures in apo, bicarbonate-bound and inhibitor-bound states. These, combined with uptake and computational studies, reveal important molecular features of substrate recognition and transport, and illuminate sterol binding sites, to elucidate distinct inhibitory mechanisms of research chemicals and prescription drugs. We further probe the substrate binding site via structure-based ligand screening, identifying an AE1 inhibitor. Together, our findings provide insight into mechanisms of solute carrier transport and inhibition.

Multiscale network analysis identifies potential receptors for SARS-CoV-2 and reveals their tissue-specific and age-dependent expression.

The coronavirus disease 2019 (COVID-19) pandemic has affected tens of millions of individuals and caused hundreds of thousands of deaths worldwide. Here, we present a comprehensive, multiscale network analysis of the transcriptional response to the virus. In particular, we focused on key regulators, cell receptors, and host processes that were hijacked by the virus for its advantage. ACE2-controlled processes involved CD300e (a TYROBP receptor) as a key regulator and the activation of IL-2 pro-inflammatory cytokine signaling. We further investigated the age dependency of such receptors in different tissues. In summary, this study provides novel insights into the gene regulatory organization during the SARS-CoV-2 infection and the tissue-specific, age-dependent expression of the cell receptors involved in COVID-19.

Multi-omics-based analysis of high grade serous ovarian cancer subtypes reveals distinct molecular processes linked to patient prognosis.

Despite advancements in treatment, high-grade serous ovarian cancer (HGSOC) is still characterized by poor patient outcomes. To understand the molecular heterogeneity of this disease, which underlies the challenge in selecting optimal treatments for HGSOC patients, we have integrated genomic, transcriptomic, and epigenetic information to identify seven new HGSOC subtypes using a multiscale clustering method. These subtypes not only have significantly distinct overall survival, but also exhibit unique patterns of gene expression, microRNA expression, DNA methylation, and copy number alterations. As determined by our analysis, patients with similar clinical outcomes have distinct profiles of activated or repressed cellular processes, including cell cycle, epithelial-to-mesenchymal transition, immune activation, interferon response, and cilium organization. Furthermore, we performed a multiscale gene co-expression network analysis to identify subtype-specific key regulators and predicted optimal targeted therapies based on subtype-specific gene expression. In summary, this study provides new insights into the cellular heterogeneity of the HGSOC genomic, epigenetic, and transcriptomic landscapes and provides a basis for future studies into precision medicine for HGSOC patients.

Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets.

Alzheimer's disease (AD), the most common form of dementia, is recognized as a heterogeneous disease with diverse pathophysiologic mechanisms. In this study, we interrogate the molecular heterogeneity of AD by analyzing 1543 transcriptomes across five brain regions in two AD cohorts using an integrative network approach. We identify three major molecular subtypes of AD corresponding to different combinations of multiple dysregulated pathways, such as susceptibility to tau-mediated neurodegeneration, amyloid-ß neuroinflammation, synaptic signaling, immune activity, mitochondria organization, and myelination. Multiscale network analysis reveals subtype-specific drivers such as GABRB2, LRP10, MSN, PLP1, and ATP6V1A We further demonstrate that variations between existing AD mouse models recapitulate a certain degree of subtype heterogeneity, which may partially explain why a vast majority of drugs that succeeded in specific mouse models do not align with generalized human trials across all AD subtypes. Therefore, subtyping patients with AD is a critical step toward precision medicine for this devastating disease.

Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer's Disease.

To identify the molecular mechanisms and novel therapeutic targets of late-onset Alzheimer's Disease (LOAD), we performed an integrative network analysis of multi-omics profiling of four cortical areas across 364 donors with varying cognitive and neuropathological phenotypes. Our analyses revealed thousands of molecular changes and uncovered neuronal gene subnetworks as the most dysregulated in LOAD. ATP6V1A was identified as a key regulator of a top-ranked neuronal subnetwork, and its role in disease-related processes was evaluated through CRISPR-based manipulation in human induced pluripotent stem cell-derived neurons and RNAi-based knockdown in Drosophila models. Neuronal impairment and neurodegeneration caused by ATP6V1A deficit were improved by a repositioned compound, NCH-51. This study provides not only a global landscape but also detailed signaling circuits of complex molecular interactions in key brain regions affected by LOAD, and the resulting network models will serve as a blueprint for developing next-generation therapeutic agents against LOAD.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Comparative effects of oncogenic mutations G12C, G12V, G13D, and Q61H on local conformations and dynamics of K-Ras.

K-Ras is the most frequently mutated protein in human cancers. However, until very recently, its oncogenic mutants were viewed as undruggable. To develop inhibitors that directly target oncogenic K-Ras mutants, we need to understand both their mutant-specific and pan-mutant dynamics and conformations. Recently, we have investigated how the most frequently observed K-Ras mutation in cancer patients, G12D, changes its local dynamics and conformations (Vatansever et al., 2019). Here, we extend our analysis to study and compare the local effects of other frequently observed oncogenic mutations, G12C, G12V, G13D and Q61H. For this purpose, we have performed Molecular Dynamics (MD) simulations of each mutant when active (GTP-bound) and inactive (GDP-bound), analyzed their trajectories, and compared how each mutant changes local residue conformations, inter-protein distance distributions, local flexibility and residue pair correlated motions. Our results reveal that in the four active oncogenic mutants we have studied, the α2 helix moves closer to the C-terminal of the α3 helix. However, P-loop mutations cause α3 helix to move away from Loop7, and only G12 mutations change the local conformational state populations of the protein. Furthermore, the motions of coupled residues are mutant-specific: G12 mutations lead to new negative correlations between residue motions, while Q61H destroys them. Overall, our findings on the local conformational states and protein dynamics of oncogenic K-Ras mutants can provide insights for both mutant-selective and pan-mutant targeted inhibition efforts.

Oncogenic G12D mutation alters local conformations and dynamics of K-Ras.

K-Ras is the most frequently mutated oncoprotein in human cancers, and G12D is its most prevalent mutation. To understand how G12D mutation impacts K-Ras function, we need to understand how it alters the regulation of its dynamics. Here, we present local changes in K-Ras structure, conformation and dynamics upon G12D mutation, from long-timescale Molecular Dynamics simulations of active (GTP-bound) and inactive (GDP-bound) forms of wild-type and mutant K-Ras, with an integrated investigation of atomistic-level changes, local conformational shifts and correlated residue motions. Our results reveal that the local changes in K-Ras are specific to bound nucleotide (GTP or GDP), and we provide a structural basis for this. Specifically, we show that G12D mutation causes a shift in the population of local conformational states of K-Ras, especially in Switch-II (SII) and α3-helix regions, in favor of a conformation that is associated with a catalytically impaired state through structural changes; it also causes SII motions to anti-correlate with other regions. This detailed picture of G12D mutation effects on the local dynamic characteristics of both active and inactive protein helps enhance our understanding of local K-Ras dynamics, and can inform studies on the development of direct inhibitors towards the treatment of K-RasG12D-driven cancers.

The interplay between viperin antiviral activity, lipid droplets and Junín mammarenavirus multiplication.

Junín arenavirus infections are associated with high levels of interferons in both severe and fatal cases. Upon Junín virus (JUNV) infection a cell signaling cascade initiates, that ultimately attempts to limit viral replication and prevent infection progression through the expression of host antiviral proteins. The interferon stimulated gene (ISG) viperin has drawn our attention as it has been highlighted as an important antiviral protein against several viral infections. The studies of the mechanistic actions of viperin have described important functional domains relating its antiviral and immune-modulating actions through cellular lipid structures. In line with this, through silencing and overexpression approaches, we have identified viperin as an antiviral ISG against JUNV. In addition, we found that lipid droplet structures are modulated during JUNV infection, suggesting its relevance for proper virus multiplication. Furthermore, our confocal microscopy images, bioinformatics and functional results also revealed viperin-JUNV protein interactions that might be participating in this antiviral pathway at lipid droplet level. Altogether, these results will help to better understand the factors mediating innate immunity in arenavirus infection and may lead to the development of pharmacological agents that can boost their effectiveness thereby leading to new treatments for this viral disease.

Intrinsic K-Ras dynamics: A novel molecular dynamics data analysis method shows causality between residue pair motions.

K-Ras is the most frequently mutated oncogene in human cancers, but there are still no drugs that directly target it in the clinic. Recent studies utilizing dynamics information show promising results for selectively targeting mutant K-Ras. However, despite extensive characterization, the mechanisms by which K-Ras residue fluctuations transfer allosteric regulatory information remain unknown. Understanding the direction of information flow can provide new mechanistic insights for K-Ras targeting. Here, we present a novel approach -conditional time-delayed correlations (CTC) - using the motions of all residue pairs of a protein to predict directionality in the allosteric regulation of the protein fluctuations. Analyzing nucleotide-dependent intrinsic K-Ras motions with the new approach yields predictions that agree with the literature, showing that GTP-binding stabilizes K-Ras motions and leads to residue correlations with relatively long characteristic decay times. Furthermore, our study is the first to identify driver-follower relationships in correlated motions of K-Ras residue pairs, revealing the direction of information flow during allosteric modulation of its nucleotide-dependent intrinsic activity: active K-Ras Switch-II region motions drive Switch-I region motions, while α-helix-3L7 motions control both. Our results provide novel insights for strategies that directly target mutant K-Ras.

Antiviral activity of an N-allyl acridone against dengue virus.

BACKGROUND: Dengue virus (DENV), a member of the family Flaviviridae, is at present the most widespread causative agent of a human viral disease transmitted by mosquitoes. Despite the increasing incidence of this pathogen, there are no antiviral drugs or vaccines currently available for treatment or prevention. In a previous screening assay, we identified a group of N-allyl acridones as effective virus inhibitors. Here, the antiviral activity and mode of action targeted to viral RNA replication of one of the most active DENV-2 inhibitors was further characterized. RESULTS: The compound 10-allyl-7-chloro-9(10H)-acridone, designated 3b, was active to inhibit the in vitro infection of Vero cells with the four DENV serotypes, with effective concentration 50% (EC50) values in the range 12.5-27.1 µM, as determined by virus yield inhibition assays. The compound was also effective in human HeLa cells. No cytotoxicity was detected at 3b concentrations up to 1000 µM. Mechanistic studies demonstrated that virus entry into the host cell was not affected, whereas viral RNA synthesis was strongly inhibited, as quantified by real time RT-PCR. The addition of exogenous guanosine together with 3b rescued only partially the infectivity of DENV-2. CONCLUSIONS: The acridone derivative 3b selectively inhibits the infection of Vero cells with the four DENV serotypes without a direct interaction with the host cell or the virion but interfering specifically with the intracellular virus multiplication. The mode of antiviral action for this acridone apparently involves the cellular enzyme inosine-monophospahe dehydrogenase together with another still unidentified target related to DENV RNA synthesis.