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Parkkila, S.

Publications and source records attributed to Parkkila, S..

13 recordsLinked to original sources

Hypoxia and Associated Acidosis Generate Cell-Type Specific Myeloid Responses in Glioblastoma

Hypoxia is a defining feature of glioblastoma (GBM), yet how it cooperates with hypoxia-associated acidosis to shape microglia and infiltrating monocyte-derived macrophages (MDM) remains poorly understood. We integrated cyclic immunohistochemistry, single-cell RNA sequencing, spatial transcriptomics, in vitro cell cultures, and DNA methylation profiling to outline hypoxia-driven responses in up to 136 GBMs. These hypoxic niches were selectively enriched for MDMs that activated carbonic anhydrase (CA) mediated pH buffering and other metabolic adaptation programs, enabling survival in acidic hypoxia, increasingly interacted with cancer cells, and show polarization toward immunosuppressive myeloid-derived suppressor cell (MDSC)-like states. In contrast, microglia were depleted in hypoxic areas, lacked compensatory CA isoenzymes, and developed TNF-linked stress responses and loss of homeostatic identity in acidic hypoxia. These findings identify metabolic adaptation to hypoxia-associated microenvironmental stress as a key determinant of GBM immune architecture, driving myeloid cell fates, spatial TME reorganization and the emergence of immunosuppressive tumor ecosystems.

cancer biology↗

TFBSFootprinter: a multiomics tool for prediction of transcription factor binding sites in vertebrate species

BackgroundTranscription factor (TF) proteins play a critical role in the regulation of eukaryotic gene expression via sequence-specific binding to genomic locations known as transcription factor binding sites (TFBSs). Accurate prediction of TFBSs is essential for understanding gene regulation, disease mechanisms, and drug discovery. These studies are therefore relevant not only in humans but also in model organisms and domesticated and wild animals. However, current tools for the automatic analysis of TFBSs in gene promoter regions are limited in their usability across multiple species. To our knowledge, no tools currently exist that allow for automatic analysis of TFBSs in gene promoter regions for many species. Methodology and FindingsThe TFBSFootprinter tool combines multiomic transcription-relevant data for more accurate prediction of functional TFBSs in 124 vertebrate species. In humans, this includes vertebrate sequence conservation (GERP), proximity to transcription start sites (FANTOM5), correlation of expression between target genes and TFs predicted to bind promoters (FANTOM5), overlap with ChIP-Seq TF metaclusters (GTRD), overlap with ATAC-Seq peaks (ENCODE), eQTLs (GTEx), and the observed/expected CpG ratio (Ensembl). TFBSFootprinter analyses are based on the Ensembl transcript ID for simplicity of use and require minimal setup steps. Benchmarking of the TFBSFootprinter on a manually curated and experimentally verified dataset of TFBSs produced superior results when using all multiomic data (average area under the receiver operating characteristic curve, 0.881), compared with DeepBind (0.798), FIMO (0.817) and traditional PWM (0.854). The results were further improved by selecting the best overall combination of multiomic data (0.910). Additionally, we determined combinations of multiomic data that provide the best model of binding for each TF. TFBSFootprinter is available as Conda and Python packages.

bioinformatics↗

Physiological modeling and biomechanical insights of cytokine modulation in COVID-19 and pulmonary fibrosis through graph-based approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Elucidating the molecular mechanism of phytochemicals against Parkinson's disease through an integrated systems biology and molecular modeling approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Mechanistic modeling of Mycobacterium tuberculosis β-carbonic anhydrase inhibitors using integrated systems biology and the QSAR approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Elucidating Molecular Mechanism and Chemical Space of Chalcones through Knowledge Graph and Machine Learning: A Combined Bio-Cheminformatic Pipeline

We developed a bio-cheminformatics method, exploring disease inhibition mechanisms using machine learning-enhanced quantitative structure-activity relationship (ML-QSAR) models and knowledge-driven neural networks. ML-QSAR models were developed using molecular fingerprint descriptors and the Random Forest algorithm to explore the chemical spaces of Chalcones inhibitors against diverse disease properties, including antifungal, anti-inflammatory, anticancer, antimicrobial, and antiviral effects. We generated and validated robust machine learning-based bioactivity prediction models ((https://ashspred.streamlit.app/) for the top genes. These models underwent ROC and applicability domain analysis, followed by molecular docking studies to elucidate the molecular mechanisms of the molecules. Through comprehensive neural network analysis, crucial genes such as AKT1, HSP90A1, SRC, and STAT3 were identified. The PubChem fingerprint-based model revealed key descriptors: PubchemFP521 for AKT1, PubchemFP180 for SRC, PubchemFP633 for HSP90, and PubchemFP145 and PubchemFP338 for STAT3, consistently contributing to bioactivity across targets. Notably, chalcone derivatives demonstrated significant bioactivity against target genes, with compound RA1 displaying a predictive pIC50 value of 5.76 against HSP90A and strong binding affinities across other targets. Compounds RA5 to RA7 also exhibited high binding affinity scores comparable to or exceeding existing drugs. These findings emphasize the importance of knowledge-based neural network-based research for developing effective drugs against diverse disease properties. These interactions warrant further in vitro and in vivo investigations to elucidate their potential in rational drug design. The presented models provide valuable insights for inhibitor design and hold promise for drug development. Future research will prioritize investigating these molecules for mycobacterium tuberculosis, enhancing the comprehension of effectiveness in addressing infectious diseases.

bioinformatics↗

Molecular Insights into Zea mays Active Phytochemicals for Diabetes and Inflammation Treatment: A Web App-Based Machine Learning and Network Pharmacology Approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Deciphering the Biochemical and Physiological Mechanisms of Mycobacterium Tuberculosis β-Carbonic Anhydrase 3 and Its Modulators through Mechanistic QSAR Model and Integrated Systems Biology Approaches

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Multimolecular feature-based machine learning: system biology enhanced RF-QSAR modeling for the efficient prediction of the inhibitory potential of diverse SARS CoV-2 3CL Protease inhibitors

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Neural Network and Random Forest Algorithms as Catalysts in QSAR/QSAAR Modeling: Targeting β-Carbonic Anhydrase for Antituberculosis Drug Design

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

Evolution is in the details: Regulatory differences in modern human and Neandertal

Transcription factor proteins play a critical role in the regulation of eukaryote gene expression via sequence- specific binding to genomic locations known as transcription factor binding sites. We studied sites of genomic variation between modern human and Neanderthal promoters. We detected significant differences in the binding affinities of 110 transcription factors to the promoters of 74 target genes. The transcription factors were enriched for terms related to vision, motor neurons, homeobox, and brain, whereas the target genes and their direct interactors were enriched in terms related to autism, brain, connective tissue, trachea, prostate, skull morphology, and vision. Secondary analysis of single-cell data revealed that a subset of the identified transcription factors (CUX1, CUX2, ESRRG, FOXP1, FOXP2, MEF2C, POU6F2, PRRX1 and RORA) co-occur as marker genes in L4 glutamatergic neurons. The majority of these genes have known roles in autism and/or schizophrenia and are associated with human accelerated regions (elevated divergence in humans vs. other primates). These results support the value of gene regulation studies for the evolution of human cognitive abilities and the neuropsychiatric disorders that accompany it.

genomics↗

Bioinformatic characterization of angiotensin-converting enzyme 2, the entry receptor for SARS-CoV-2

The World Health Organization declared the COVID-19 epidemic a public health emergency of international concern on March 11th, 2020, and the pandemic is rapidly spreading worldwide. COVID-19 is caused by a novel coronavirus SARS-CoV-2, which enters human target cells via angiotensin converting enzyme 2 (ACE2). We used a number of bioinformatics tools to computationally characterize ACE2 by determining its cell-specific expression in trachea, lung, and small intestine, derive its putative functions, and predict transcriptional regulation. The small intestine expressed higher levels of ACE2 than any other organ. The large intestine, kidney and testis showed moderate signals, whereas the signal was weak in the lung. Single cell RNA-Seq data from trachea indicated positive signals along the respiratory tract in key protective cell types including club, goblet, proliferating, and ciliary epithelial cells; while in lung the ratio of ACE2-expressing cells was low in all cell types (<2.6%), but was highest in vascular endothelial and goblet cells. Gene ontology analysis suggested that, besides its classical role in renin-angiotensin system, ACE2 may be functionally associated with angiogenesis/blood vessel morphogenesis. Using a novel tool for the prediction of transcription factor binding sites we identified several putative binding sites within two tissue-specific promoters of the ACE2 gene. Our results also confirmed that age and gender play no significant role in the regulation of ACE2 mRNA expression in the lung. IMPORTANCEVaccines and new medicines are urgently needed to prevent spread of COVID-19 pandemic, reduce the symptoms, shorten the duration of disease, prevent virus spread in the body, and most importantly to save lives. One of the key drug targets could be angiotensin-converting enzyme 2 (ACE2), which is a crucial receptor for the corona virus (SARS-CoV-2). It is known that SARS coronavirus infections lead to worse outcome in the elderly and in males. Therefore, one aim of the present study was to investigate whether age or sex could contribute to the regulation of ACE2 expression. We also decided to explore the transcriptional regulation of ACE2 gene expression. Since data on ACE2 distribution is still conflicting, we aimed to get a more comprehensive view of the cell types expressing the receptor of SARS-CoV-2. Finally, we studied the coexpression of ACE2 with other genes and explored its putative functions using gene ontology enrichment analysis.

bioinformatics↗