Deciphering the impact of genomic variation on function.

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

Deep learning large-scale drug discovery and repurposing.

Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing.

Publication, funding, and experimental data in support of Human Reference Atlas construction and usage.

Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.

Multiscale topology classifies cells in subcellular spatial transcriptomics.

Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.

Sex differences orchestrated by androgens at single-cell resolution.

Sex differences in mammalian complex traits are prevalent and are intimately associated with androgens1-7. However, a molecular and cellular profile of sex differences and their modulation by androgens is still lacking. Here we constructed a high-dimensional single-cell transcriptomic atlas comprising over 2.3 million cells from 17 tissues in Mus musculus and explored the effects of sex and androgens on the molecular programs and cellular populations. In particular, we found that sex-biased immune gene expression and immune cell populations, such as group 2 innate lymphoid cells, were modulated by androgens. Integration with the UK Biobank dataset revealed potential cellular targets and risk gene enrichment in antigen presentation for sex-biased diseases. This study lays the groundwork for understanding the sex differences orchestrated by androgens and provides important evidence for targeting the androgen pathway as a broad therapeutic strategy for sex-biased diseases.

Bridging structural and cell biology with cryo-electron microscopy.

Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.

Complementing Cell Taxonomies with a Multicellular Analysis of Tissues.

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.

Recording physiological history of cells with chemical labeling.

Recordings of the physiological history of cells provide insights into biological processes, yet obtaining such recordings is a challenge. To address this, we introduce a method to record transient cellular events for later analysis. We designed proteins that become labeled in the presence of both a specific cellular activity and a fluorescent substrate. The recording period is set by the presence of the substrate, whereas the cellular activity controls the degree of the labeling. The use of distinguishable substrates enabled the recording of successive periods of activity. We recorded protein-protein interactions, G protein-coupled receptor activation, and increases in intracellular calcium. Recordings of elevated calcium levels allowed selections of cells from heterogeneous populations for transcriptomic analysis and tracking of neuronal activities in flies and zebrafish.

Gravity-Oriented Microfluidic Device for Biocompatible End-to-End Fabrication of Cell-Laden Microgels.

Droplet microfluidics are extensively utilized to generate monodisperse cell-laden microgels in biomedical applications. However, maintaining cell viability is still challenging due to overexposure to harsh conditions in subsequent procedures that recover the microgels from the oil phase. Here, a gravity-oriented microfluidic device for end-to-end fabrication of cell-laden microgels is reported, which integrates dispersion, gelation, and extraction into a continuous workflow. This innovative on-chip extraction, driven by native buoyancy and kinetically facilitated by pseudosurfactant, exhibits 100% retrieval efficiency for microgels with a wide range of sizes and stiffnesses. The viability of encapsulated cells is perfectly maintained at ≈98% with minimal variations within and between batches. The end-to-end fabrication remarkably enhances the biocompatibility and practicality of microfluidics-based cell encapsulation and is promising to be compatible with various applications ranging from single-cell analysis to clinical therapy.

Cell-type-directed design of synthetic enhancers.

Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes1. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models2-6, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create 'dual-code' enhancers that target two cell types and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

The free energy landscape of small-world networks of cells.

The properties of organs, tissues, organoids, and other systems of cells, are influenced by the spatial localization and distribution of their elements. Here, we used networks to describe distributions of cells on a surface where the small-world coefficient (SW) of the networks was varied between SW~1 (random uniform distributions) and SW~10 (clustered distributions). The small-world coefficient is a topological measure of graphs: networks with SW>1 are topologically biased to transmit information. For each system configuration, we then determined the total energy U as the sum of the energies that describe cell-cell interactions - approximated by a harmonic potential. The graph of energy (U) across the configuration space of the networks (SW) is the energy landscape: it indicates which configuration a system of cells will likely assume over time. We found that, depending on the model parameters, the energy landscapes of 2D distributions of cells may be of different types: from type I to type IV. Type I and type II systems have high probability to evolve into random distributions. Type III and type IV systems have a higher probability to form clustered architectures. A great many of simulations indicated that cultures of cells with high initial density and limited sensing range could evolve into clustered configurations with enhanced topological characteristics. Moreover, the strongest the binding between cells, the greater the likelihood that they will assume configurations characterized by finite values of SW. Results of the work are relevant for those working the field of tissue engineering, regenerative medicine, the formation of in-vitro-models, the analysis of neuro-degenerative diseases.

Aplicação de ferramentas de quimioinformática na comparação da estrutura tridimensional da dihidrofolato redutase expressa em diferentes organismos / Application of cheminformatics tools for comparing dihydrofolate reductase's tridimensional structure expressed by different organisms

A quimioinformática, definida como o emprego de técnicas informáticas na solução de problemas da química, evolui em conjunto com o desenvolvimento de ferramentas computacionais e é de grande relevância para o planejamento racional de fármacos ao otimizar etapas do desenvolvimento de novas moléculas e economizar recursos e tempo. Dentre as técnicas disponíveis destacam-se o planejamento de fármacos baseado na estrutura e no ligante, que quando combinadas auxiliam na identificação e otimização de moléculas ativas frente a alvos farmacológicos. A Dihidrofolato Redutase (DHFR) é uma importante enzima da via dos folatos que catalisa a redução do dihidrofolato em tetrahidrofolato, utilizando NADPH como cofator, reação essencial para a replicação celular, visto que este ciclo resulta na síntese de precursores das bases nitrogenadas que compõem o DNA, consequentemente, inibidores de DHFR são utilizados no tratamento de infecções bacterianas e alguns tipos de câncer. Trypanosoma cruzi, protozoário causador da doença de chagas, é um dos organismos que expressam a DHFR, além do próprio Homo sapiens. Analisaram-se ligantes conhecidos e as estruturas da proteína expressa pelos dois organismos, visando identificar pontos de divergência que possam ser explorados no planejamento de moléculas seletivas para o tratamento da doença de Chagas. Os 6 modelos cristalográficos de T. cruzi e 2 de H. sapiens foram obtidos do banco de dados de proteínas (PDB) após aplicação de filtros de qualidade. Foram analisadas as sequências de aminoácidos dos modelos, com o uso do Cluster Ômega, sua estrutura tridimensional com os programas Pymol e Chimera X, além da análise das cavidades proteicas com o CavityPlus, que também gerou os farmacóforos de ambos alvos. A análise de estrutura primária identificou mutações em três aminoácidos nos cristais do parasita, que podem ser explicados por diferentes caminhos evolutivos de grupos segregados, embora nenhuma mutação observada esteja em regiões de sítio ativo. A análise dos modelos permitiu que fossem identificados os 25 aminoácidos que estão a menos de 5 Å de distância dos ligantes de T. cruzi, sendo 5 aminoácidos responsáveis por interações de hidrogênio com pelo menos um dos ligantes analisados. Destes, 18 se repetem na proteína humana ou são substituídos por outro aminoácido que mantém a mesma interação. Quanto às diferenças observadas, destacam-se a asparagina 44 substituída por uma prolina na proteína humana e a prolina 92, substituída por uma lisina. A análise de cavidades identificou três cavidades em cada proteína, embora somente as cavidades correspondentes ao sítio ativo sejam druggables. A cavidade da proteína humana é maior e mais alongada, além de apresentar o aspecto de um túnel, enquanto a cavidade da proteína parasita é mais aberta, tal abertura permite que ligantes com o anel benzeno meta substituídos explorem uma região existente na cavidade de T. cruzi que é fechada na humana. O farmacóforo de ambas proteínas foi identificado, apresentando diferenças no tamanho e angulação que também podem ser explorados no planejamento de fármacos seletivos

Chemoinformatic, defined as the use of informatic techniques to solve chemical problems, has evolved together with new computational tools and it is quite important for rational drug designing, by optimizing different steps on the development pipeline of new molecules, saving resources and time. From all the available tools, structure and ligand based drug design shall be highlighted, when combined, they support the identification and optimization of active molecules from pharmaceutical targets. Dihydrofolate reductase (DHFR) is an important enzyme of the folate pathway that catalyzes the reduction of dihydrofolate to tetrahydrofolate, by using NADPH as cofactor. This reaction is essential for cell replication, as this pathway results in the synthesis of nucleobases that build the DNA. That's the reason why DHFR inhibitors are used for treating bacterial infections and some types of cancer. Trypanosoma cruzi, a protozoa that causes Chagas disease, is one of the organisms that express DHFR, besides Homo sapiens itself. This work analyzed known ligands and the structure of the protein expressed by both organisms, aiming to identify divergence points that could be explored for designing selective drugs for Chagas disease treatment. The 6 proteins crystallographic models from T. cruzi and 2 from H. sapiens were obtained from protein data bank (PDB) after the application of quality filters. The amino acid sequence of each model was analyzed by Clustal Omega, its tridimensional structure by Pymol and Chimera X and the cavity analysis by CavityPlus, that also generated the pharmacophore from both targets. The primary structure analysis identified mutations on three amino acids on the parasite christal, which may be explained by different evolutive paths from segregated groups, although none of the observed mutations are on the active site region. The model's analysis allowed the identification of 24 amino acids that are closer than 5 Å from the T. cruzi ligands, 5 of them responsible for hydrogen interactions on at least one of the ligands analyzed. 18 of them are repeated on the human protein or are replaced by another amino acid that preserves the same interaction. As by the differences observed that shall be highlighted, asparagine 44 is replaced by a proline on the human protein, and proline 92 by a lysin. The cavity analysis identified three cavities on each protein, although only the cavities of the active site are druggables. The human protein cavity is bigger and longer, besides it looks like its a tunnel, when the parasite protein is open, that opening allows ligands with benzene ring meta substituted to explore the existing regions of the T. cruzi protein that is closed on the human protein. Lastly, the pharmacophore from both proteins was identified, it shows differences on size and angulation that also could be explored in the designing of selective drugs

nteraction between FAK/αB-crystalline is important for viability of the glioblastoma cells

Abstract Glioblastoma multiforme is a tumor of the central nervous system. Focal Adhesion Kinase (FAK) and αB-crystalline are two proteins involved in glioblastoma development. In this study, we investigated whether the FAK/αB-crystalline interaction is important for glioblastoma cells, we aimed to investigate the interaction of these two proteins in the glioblastoma multiforme cell line U87-MG. Two peptides named FP01 peptide (derived from αB-crystalline) and FP02 peptide (derived from FAK) were synthesized for this study. Treatment of U87-MG with the peptides FP01 and FP02 in the concentration at 50 µM reduced the viability cellular to around 41% and 51%, respectively. Morphological alterations in the cells treated with the peptides when compared to the control were observed. This study suggests that the interaction between FAK and αB-crystalline is important for the viability of glioblastoma cells

Human venous blood derivatives as fetal bovine serum substitute for fibroblast culture cells in a fibrin construct

Aim: Venous blood derivatives (VBDs) have been suggested as substitutes for Fetal Bovine Serum (FBS) to improve the clinical transition of cell-based therapies. The literature is not clear about which is the best VBDs substitute. The present study aimed to evaluate the influence of VBDs on cell viability and describe a new method to seed these cells in a 3D Platelet-Rich Fibrin (PRF). Methods: Blood was processed to obtain Platelet-Poor Plasma from PRF (P-PRF), Human Serum (HS), Platelet-Poor Plasma from PRP (P-PRP), activated-PRP (a-PRP), and Platelet lysate (PL). Cells were supplemented with each VBD at 10% and FBS at 10% was the control. Cell viability (fibroblast 3T3/NIH) test was evaluated with MTT assay in two ways: i) cell-seeded and expanded with VBD; ii) cell-seed with FBS and expanded with VBD. To seed the Fibrin construct, cells were suspended in PBS and dropped into the blood sample before performing Choukroun's protocol for PRF. Constructs were cultured for 7 days in VBD supplements and FBS. Histological and Immunohistochemical analysis with vimentin was performed. Cell viability was analyzed by one-way ANOVA. Results: VBD's production time was very heterogeneous. Cells expanded in HS and a-PRP has grown faster. VBD-supplemented culture media provided cell culture highly sensible to trypsin/EDTA 0.25%. Cells seeded and expanded with VBD presented viability comparable to FBS in HS, a-PRP, and P-PRP (p>0.05) and lower in P-PRF and PL groups (p<0.05). The viability of cell seed with FBS and expanded with VBD was similar between P-PRF, a-PRP, PL, and FBS (p>0.05) and lower in HS and P-PRP (p<0.005). PRF-seeded cells showed a positive expression of vimentin and were able to maintain all cells supplemented with VBD. Conclusion: VBD supplements were able to maintain fibroblast cells in 2D and 3D cultures. The new method of the fibrin-cell construct was efficient to insert the cells into the fibrin network

Uma nova variante de splicing STK3/MST2 que exibe atividade de quinase reduzida e potencial clínico / A novel STK3/MST2 splicing variant displays impaired kinase activity and clinical potential

A via Hippo consiste em uma cascata de serina-treonina quinases que desempenha um papel central na transdução de sinais mecânicos. Em mamíferos, o eixo canônico da via consiste na ativação das quinases MST1 e MST2 (codificadas pelos genes STK4 e STK3, respectivamente) e LATS1 e LATS2. A ativação dos dois últimos culmina na fosforilação, retenção citoplasmática e inativação dos coativadores transcricionais YAP e TAZ. A inativação de Hippo resulta na localização nuclear de YAP/TAZ, aumento da proliferação e contribui para a transformação maligna em células epiteliais. No presente trabalho, identificamos que o exon 7, que codifica um segmento do domínio quinase de MST2, estava ausente em células malignas da glândula mamária humana, T4-2, mas não na linhagem não maligna, S1. A exclusão do exon 7 compromete a interação de MST2 com MOB1, um dos principais substratos de MST2. Ao contrário da proteína completa, a superexpressão de MST2 sem o exon 7 não resultou em aumento da morte celular, bem como, não diminuiu a proliferação celular. Esta nova variante de STK3/MST2, a qual denominamos STK3Δ7/MST2Δ7 é produto de um exon skipping e foi encontrada em amostras de tumores de pacientes, mas pouco predominante em amostras de tecidos normais. Além disso, em pacientes com câncer pancreático, a expressão STK3Δ7 resultou em menor sobrevida específica da doença. A retenção do exon 7 foi menor em tumores mais agressivos e com alto grau histológico. Em ensaio 3D, células não malignas com expressão ectópica de MST2Δ7 não respondem aos sinais inibitórios da membrana basal reconstituída e formam estruturas tumor-like. Esta nova variante perde sua atividade quinase e pode perturbar a homeostase tecidual pela incapacidade de ativar morte e inibir a proliferação celular, mesmo em microambientes repressores desses processos em células normais, como na presença membrana basal. Esses achados podem avançar o nosso conhecimento sobre progressão tumoral com possível relevância clínica

The Hippo pathway consists of a cascade of serine-threonine kinases that plays a central role in the transduction of mechanical signals. In mammals, the canonical axis of the pathway consists of the activation of the kinases MST1 and MST2 (encoded by the genes STK4 and STK3, respectively) and LATS1 and LATS2 and their activation culminates in the phosphorylation, cytoplasmic retention and inactivation of the transcriptional coactivators YAP and TAZ. Hippo inactivation results in nuclear localization of YAP/TAZ, increased cell proliferation, and contributes to malignant transformation in epithelial cells. In the present work, we identified that exon 7, which encodes a segment of the kinase domain of MST2, was absent in malignant cells of the human mammary gland, T4-2, but not in the non-malignant S1 cell line. Exclusion of exon 7 compromises the interaction of MST2 with one of its main substrates, MOB1. Unlike the full-length protein, overexpression of MST2 without exon 7 did not result in increased cell death, nor decreased cell proliferation. This new variant of STK3/MST2, which we named STK3Δ7/MST2Δ7, is the product of an exon skipping and was found in tumor samples, but seldomly found in samples of normal tissues. Furthermore, in patients with pancreatic cancer, STK3Δ7 expression resulted in lower disease-specific survival. Exon 7 retention was reduced in aggressive tumors with a high histological grade. In a 3D assay, non-malignant cells with ectopic expression of MST2Δ7, even at low concentrations, do not respond to inhibitory signals from a reconstituted basement membrane and form tumor-like structures. This new variant loses its kinase activity and may disturb the tissue homeostasis due to its inability to activate death and to inhibit cell proliferation, even in microenvironments that repress these processes in normal cells, such as the basement membrane. These findings may advance our knowledge about tumor progression and might be clinically relevant