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Hypothalamic and Cell-Specific Transcriptomes Unravel a Dynamic Neuropil Remodeling in Leptin-Induced and Typical Pubertal Transition in Female Mice

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ISCIENCE 2020年第10期23卷 101563页

作者： Han, Xingfa Burger, Laura L. Garcia-Galiano, David Sim, Seokmin Allen, Susan J. Olson, David P. Myers, Martin G., Jr. Elias, Carol F. Univ Michigan Dept Mol & Integrat Physiol Ann Arbor MI 48109 USA Sichuan Agr Univ Isotope Res Lab Yaan 625014 Peoples R China Univ Michigan Dept Pediat & Communicable Dis Ann Arbor MI 48109 USA Univ Michigan Dept Internal Med Div Metab Endocrinol & Diabet Ann Arbor MI 48109 USA Univ Michigan Dept Obstet & Gynecol Ann Arbor MI 48109 USA

Epidemiological and genome-wide association studies ( GWAS) have shown high correlation between childhood obesity and advance in puberty. Early age at menarche is associated with a series of morbidities, including breast cancer, cardiovascular diseases, type 2 diabetes, and obesity. The adipocyte hormone leptin signals the amount of fat stores to the neuroendocrine reproductive axis via direct actions in the brain. Using mouse genetics, we and others have identified the hypothalamic ventral premammillary nucleus (PMv) and the agouti-related protein (AgRP) neurons in the arcuate nucleus (Arc) as primary targets of leptin action in pubertal maturation. However, the molecular mechanisms underlying leptin's effects remain unknown. Here we assessed changes in the PMv and Arc transcriptional program during leptin-stimulated and typical pubertal development using overlapping analysis of bulk RNA sequecing, TRAP sequencing, and the published database. Our findings demonstrate that dynamic somatodendritic remodeling and extracellular space organization underlie leptin-induced and typical pubertal maturation in female mice.

关键词： Neuroscience computational bioinformatics Transcriptomics

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Dissecting molecular, pathological, and clinical features associated with tumor neural/neuroendocrine heterogeneity

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ISCIENCE 2023年第6期26卷 106983页

作者： Cai, Ling DeBerardinis, Ralph J. Xiao, Guanghua Minna, John D. Xie, Yang UT Southwestern Med Ctr Quantitat Biomed Res Ctr Peter ODonnell Sch Publ Hlth Dallas TX 75390 USA UT Southwestern Med Ctr Childrens Res Inst Dallas TX 75390 USA UT Southwestern Med Ctr Simmons Comprehens Canc Ctr Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Howard Hughes Med Inst Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dept Bioinformat Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Hamon Ctr Therapeut Oncol Res Dallas TX 75390 USA UT Southwestern Med Ctr Dept Pharmacol Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dept Internal Med Dallas TX 75390 USA

Lineage plasticity, especially transdifferentiation between neural/neuroendo-crine (NE) and non-NE lineage, has been observed in multiple cancer types and linked to increased tumor aggressiveness. However, existing NE/non-NE sub-type classifications in various cancer types were established through ad hoc ap-proaches in different studies, making it difficult to align findings across cancer types and extend investigations to new datasets. To address this issue, we developed a generalized strategy to generate quantitative NE scores and a web application to facilitate its implementation. We applied this method to nine datasets covering seven cancer types, including two neural cancers, two neuroendocrine cancers, and three non-NE cancers. Our analysis revealed significant NE inter -tu-moral heterogeneity and identified strong associations between NE scores and molecular, histological, and clinical features, including prognosis in different can-cer types. These results support the translational utility of NE scores. Overall, our work demonstrated a broadly applicable strategy for determining the NE prop-erties of tumors.

关键词： computational bioinformatics Cancer systems biology Cancer

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A statistical approach for identifying single nucleotide variants that affect transcription factor binding

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ISCIENCE 2024年第5期27卷 109765页

作者： Baumgarten, Nina Rumpf, Laura Kessler, Thorsten Schulz, Marcel H. Goethe Univ Inst Cardiovasc Regenerat D-60590 Frankfurt Germany Goethe Univ Inst Computat Genom Med D-60590 Frankfurt Germany Goethe Univ Inst Comp Sci D-60629 Frankfurt Germany German Ctr Cardiovasc Res Partner Site Rhein Main D-60590 Frankfurt Germany Tech Univ Munich German Heart Ctr Munich Sch Med & Hlth Dept Cardiol D-80636 Munich Germany German Ctr Cardiovasc Res Partner Site Munich Heart Alliance D-80636 Munich Germany

Non -coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo datasets showed that our approach improves upon an existing method in terms of performance and speed. Applications on eQTLs and on a genome-wide association study illustrate the usefulness of our statistics by highlighting cell type -specific regulators and target genes. An implementation of our approach is freely available on GitHub and as bioconda package.

关键词： Genomics computational bioinformatics computational mathematics

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Integrating extrusion complex-associated pattern to predict cell type-specific long-range chromatin loops

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ISCIENCE 2022年第12期25卷

作者： Deng, Yajing Tang, Li Zhou, Xiaolong Wang, Wenkang Li, Min Cent South Univ Sch Comp Sci & Engn Hunan Prov Key Lab Bioinformat Changsha 410083 Peoples R China Xinjiang Univ Sch Software Urumqi 830091 Peoples R China

The chromatin loop plays a critical role in the study of gene expression and disease. Supervised learning-based algorithms to predict the chromatin loops require large priori information to satisfy the model construction, while the pre-diction sensitivity of unsupervised learning-based algorithms is still unsatisfacory. Therefore, we propose an unsupervised algorithm, Ecomap-loop. It takes advantage of extrusion complex-associated patterns, including CTCF, RAD21, and SMC enrichments, as well as the orientation distribution of CTCF motif of loops to build feature matrices;then the eigen decomposition model is employed to obtain the cell type-specific loops. We compare the performance of Ecomap-loop with the state-of-the-art unsupervised algorithm using Hi-C, ChIA-PET, expression quantitative trait locus (eQTL), and CRISPR interference (CRISPRi) screen data;the results show that Ecomap-loop achieves the best in four cell types. In addition, the functional analysis reveals the ability of Ecomap-loop to predict active functionality-related and cell type-specific loops.

关键词： computational bioinformatics genomic analysis methodology in biological sciences Computing methodology

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A data-driven computational model for obesity-driven diabetes onset and remission through weight loss

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ISCIENCE 2023年第11期26卷 108324页

作者： Yildirim, Vehpi Sheraton, Vivek M. Brands, Ruud Crielaard, Loes Quax, Rick van Riel, Natal A. W. Stronks, Karien Nicolaou, Mary Sloot, Peter M. A. Univ Amsterdam Amsterdam Univ Dept Publ & Occupat Hlth Med Ctr NL-1081 BT Amsterdam Netherlands Univ Amsterdam Inst Adv Study NL-1012 GC Amsterdam Netherlands Univ Amsterdam Computat Sci Lab NL-1098 XH Amsterdam Netherlands Univ Amsterdam Ctr Expt & Mol Med Acad Med Ctr Med Ctr NL-1100 DD Amsterdam Netherlands AMRIF BV Agro Business Pk NL-6708 PW Wageningen Netherlands Univ Utrecht Inst Risk Assessment Sci NL-3584 CL Utrecht Netherlands Eindhoven Univ Technol Dept Biomed Engn NL-5600 MB Eindhoven Netherlands Univ Amsterdam Dept Expt & Vasc Med Med Ctr NL-1100 DD Amsterdam Netherlands

Obesity is a major risk factor for the development of type 2 diabetes (T2D), where a sustained weight loss may result in T2D remission in individuals with obesity. To design effective and feasible intervention strategies to prevent or reverse T2D, it is imperative to study the progression of T2D and remission together. Unfortunately, this is not possible through experimental and observational studies. To address this issue, we introduce a data-driven computational model and use human data to investigate the progression of T2D with obesity and remission through weight loss on the same timeline. We identify thresholds for the emergence of T2D and necessary conditions for remission. We explain why remission is only possible within a window of opportunity and the way that window depends on the progression history of T2D, individual's metabolic state, and calorie restrictions. These findings can help to optimize therapeutic intervention strategies for T2D prevention or treatment.

关键词： Human metabolism bioinformatics computational bioinformatics

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Pseudo-temporal dynamics of chemoresistant triple negative breast cancer cells reveal EGFR/HER2 inhibition as synthetic lethal during mid-neoadjuvant chemotherapy

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ISCIENCE 2023年第2期26卷 106064页

作者： Song, Won -Min Chia, Pei-Ling Zhou, Xianxiao Walsh, Martin Silva, Jose Zhang, Bin Icahn Sch Med Mt Sinai Dept Genet & Genom Sci One Gustave L Levy Pl New York NY 10029 USA Icahn Sch Med Mt Sinai Mt Sinai Ctr Transformat Dis Modeling One Gustave L Levy Pl New York NY 10029 USA Icahn Sch Med Mt Sinai Icahn Inst Data Sci & Genom Technol One Gustave L Levy Pl New York NY 10029 USA Icahn Sch Med Mt Sinai Dept Oncol Sci One Gustave L Levy Pl New York NY 10029 USA Icahn Sch Med Mt Sinai Dept Pharmacol Sci One Gustave L Levy Pl New York NY 10029 USA ASTAR Inst Mol & Cell Biol 61 Biopolis Dr Singapore 138673 Singapore Icahn Sch Med Mt Sinai Dept Pathol One Gustave L Levy Pl New York NY 10029 USA

In the absence of targetable hormonal axes, chemoresistance for triple-negative breast cancer (TNBC) often compromises patient outcomes. To investigate the underlying tumor dynamics, we performed trajectory analysis on the single-nuclei RNA-seq (snRNA-seq) of chemoresistant tumor clones during neoadjuvant chemotherapy (NAC). It revealed a common tumor trajectory across multiple pa-tients with HER2-like expansions during NAC. Genome-wide CRISPR-Cas9 knock-out on mammary epithelial cells revealed chemosensitivity-promoting knock-outs were up-regulated along the tumor trajectory. Furthermore, we derived a consensus gene signature of TNBC chemoresistance by comparing the trajectory transcriptome with chemoresistant transcriptomes from TNBC cell lines and poor prognosis patient samples to predict FDA-approved drugs, including afatinib (pan-HER inhibitor), targeting the consensus signature. We validated the syner-gistic efficacy of afatinib and paclitaxel in chemoresistant TNBC cells and confirmed pharmacological suppression of the consensus signature. The study provides a dynamic model of chemoresistant tumor transcriptome, and computa-tional framework for pharmacological intervention.

关键词： Oncology Cellular therapy computational bioinformatics

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Approximated gene expression trajectories for gene regulatory network inference on cell tracks

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ISCIENCE 2024年第9期27卷 110840页

作者： Spiess, Kay Taylor, Shannon E. Fulton, Timothy Toh, Kane Saunders, Dillan Hwang, Seongwon Wang, Yuxuan Paige, Brooks Steventon, Benjamin Verd, Berta Univ Cambridge Dept Genet Cambridge England Alan Turing Inst London England Univ Oxford Dept Biol Oxford England UCL Ctr Artificial Intelligence London England

The study of pattern formation has benefited from our ability to reverse-engineer gene regulatory network (GRN) structure from spatiotemporal quantitative gene expression data. Traditional approaches have focused on systems where the timescales of pattern formation and morphogenesis can be separated. Unfortunately, this is not the case in most animal patterning systems, where pattern formation and morphogenesis are co-occurring and tightly linked. To elucidate patterning mechanisms in such systems we need to adapt our GRN inference methodologies to include cell movements. In this work, we fill this gap by integrating quantitative data from live and fixed embryos to approximate gene expression trajectories (AGETs) in single cells and use these to reverse-engineer GRNs. This framework generates candidate GRNs that recapitulate pattern at the tissue level, gene expression dynamics at the single cell level, recover known genetic interactions and recapitulate experimental perturbations while incorporating cell movements explicitly for the first time.

关键词： Cell biology computational bioinformatics Omics

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Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

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ISCIENCE 2022年第6期25卷 104395页

作者： Jenner, Adrianne L. Smalley, Munisha Goldman, David Goins, William F. Cobbs, Charles S. Puchalski, Ralph B. Chiocca, E. Antonio Lawler, Sean Macklin, Paul Goldman, Aaron Craig, Morgan Univ Montreal Dept Math & Stat Montreal PQ Canada St Justine Univ Hosp Res Ctr Montreal PQ Canada Brigham & Womens Hosp Div Engn Med 75 Francis St Boston MA 02115 USA 7730E BlackCrest Pl Tucson AZ USA Univ Pittsburgh Dept Microbiol & Mol Genet Pittsburgh PA USA Swedish Neurosci Inst Ben & Catherine Ivy Ctr Adv Brain Tumor Treatment Seattle WA USA Harvard Med Sch Brigham & Womens Hosp Dept Neurosurg Boston MA 02115 USA Indiana Univ Dept Intelligent Syst Engn Bloomington IN USA Harvard Med Sch Dept Med Boston MA 02115 USA Queensland Univ Technol Brisbane Qld Australia TCR2 Therapeut Inc Translat Sci Cambridge MA USA Brown Univ Brown Canc Ctr Dept Pathol & Lab Med Providence RI 02912 USA

Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in the treatment of some tumors, OV therapy for central nervous system cancers has failed to show efficacy. We used an ex vivo tumor model developed from human glioblastoma tissue to evaluate the infiltration of herpes simplex OV rQNestin (oHSV-1) into glioblastoma tumors. We next leveraged our data to develop a computational, model of glioblastoma dynamics that accounts for cellular interactions within the tumor. Using our computational model, we found that low stromal density was highly predictive of oHSV-1 therapeutic success, suggesting that the efficacy of oHSV-1 in glioblastoma may be determined by stromal-to-tumor cell regional density. We validated these findings in heterogenous patient samples from brain metastatic adenocarcinoma. Our integrated modeling strategy can be applied to suggest mechanisms of therapeutic responses for central nervous system cancers and to facilitate the successful translation of OVs into the clinic.

关键词： Immunology computational bioinformatics Cancer

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Modeling the Control of TGF-β/Smad Nuclear Accumulation by the Hippo Pathway Effectors, Taz/Yap

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ISCIENCE 2020年第8期23卷 101416页

作者： Labibi, Bita Bashkurov, Mikhail Wrana, Jeffrey L. Attisano, Liliana Univ Toronto Dept Biochem Toronto ON M5S 1A8 Canada Univ Toronto Donnelly Ctr Toronto ON M5S 3E1 Canada Mt Sinai Hosp Lunenfeld Tanenbaum Res Inst Toronto ON M5G 1X5 Canada Univ Toronto Dept Mol Genet Toronto ON M5S 1A8 Canada

Integration of transforming growth factor beta (TGF-beta) signals with those of other pathways allows for precise temporal and spatial control of gene expression patterns that drive development and homeostasis. The Hippo pathway nuclear effectors, Taz/Yap, interact with the TGF-beta transcriptional mediators, Smads, to control Smad activity. Key to TGF-beta signaling is the nuclear localization of Smads. Thus, to investigate the role of Taz/Yap in Smad nuclear accumulation, we developed mathematical models of Hippo and TGF-beta cross talk. The models were based on experimental measurements of TGF-beta-induced changes in Taz/Yap and Smad subcellular localization obtained using high-throughput immunofluorescence (IF) imaging in the mouse mammary epithelial cell line, EpH4. Bayesian MCMC DREAM parameter estimation was used to quantify the uncertainty in estimates of the kinetic parameters. Variation of the model parameters and statistical analysis show that our modeling predicts that Taz/Yap can alter TGF-beta receptor activity and directly or indirectly act as nuclear retention factors.

关键词： Biological Sciences Cell Biology computational bioinformatics Integrative Aspects of Cell Biology Molecular Network

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Rapid methods for the evaluation of fluorescent reporters in tissue clearing and the segmentation of large vascular structures

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ISCIENCE 2021年第6期24卷 102650页

作者： Kirschnick, Nils Drees, Dominik Redder, Esther Erapaneedi, Raghu da Graca, Abel Pereira Schafers, Michael Jiang, Xiaoyi Kiefer, Friedemann Univ Munster European Inst Mol Imaging Waldeyerstr 15 D-48149 Munster Germany Univ Munster Inst Comp Sci Einsteinstr 62 D-48149 Munster Germany Carl Zeiss Microscopy GmbH Zeiss Res Microscopy Solut Zeiss Microscopy Customer Ctr Europe Rudolf Eber Str 2 D-73447 Oberkochen Germany

Light sheet fluorescence microscopy (LSFM) of large tissue samples does not require mechanical sectioning and allows efficient visualization of spatially complex or rare structures. Therefore, LSFMhas become invaluable in developmental and biomedical research. Because sample size may limit whole-mount staining, LSFM benefits from transgenic reporter organisms expressing fluorescent proteins (FPs) and, however, requires optical clearing and computational data visualization and analysis. The former often interferes with FPs, while the latter requires massive computing resources. Here, we describe 3D-polymerized cell dispersions, a rapid and straightforward method, based on recombinant FP expression in freely selectable tester cells, to evaluate and compare fluorescence retention in different tissue-clearing protocols. For the analysis of large LSFM data, which usually requires huge computing resources, we introduce a refined, interactive, hierarchical random walker approach that is capable of efficient segmentation of the vasculature in data sets even on a consumer grade PC.

关键词： Optical imaging computational bioinformatics

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