Systems Biology Research Articles

Genetic correlations, shared risk genes and immunity landscapes between COVID-19 and venous thromboembolism: evidence from GWAS and bulk transcriptome data.

Patients with coronavirus disease 2019 (COVID-19) were vulnerable to venous thromboembolism (VTE), which further increases the risk of unfavorable outcomes. However, neither genetic correlations nor shared genes underlying COVID-19 and VTE are well understood. This study aimed to characterize genetic correlations and common pathogenic mechanisms between COVID-19 and VTE. We used linkage disequilibrium score (LDSC) regression and Mendelian Randomization (MR) analysis to investigate the genetic associations and causal effects between COVID-19 and VTE, respectively. Then, the COVID-19 and VTE-related datasets were obtained from the Gene Expression Omnibus (GEO) database and analyzed by bioinformatics and systems biology approaches with R software, including weighted gene co-expression network analysis (WGCNA), enrichment analysis, and single-cell transcriptome sequencing analysis. The miRNA-genes and transcription factor (TF)-genes interaction networks were conducted by NetworkAnalyst. We performed the secondary analysis of the ATAC-seq and Chip-seq datasets to address the epigenetic-regulating relationship of the shared genes. This study demonstrated positive correlations between VTE and COVID-19 by LDSC and bidirectional MR analysis. A total of 26 potential shared genes were discovered from the COVID-19 dataset (GSE196822) and the VTE dataset (GSE19151), with 19 genes showing positive associations and 7 genes exhibiting negative associations with these diseases. After incorporating two additional datasets, GSE164805 (COVID-19) and GSE48000 (VTE), two hub genes TP53I3 and SLPI were identified and showed up-regulation and diagnostic capabilities in both illnesses. Furthermore, this study illustrated the landscapes of immune processes in COVID-19 and VTE, revealing the downregulation in effector memory CD8+ T cells and activated B cells. The single-cell sequencing analysis suggested that the hub genes were predominantly expressed in the monocytes of COVID-19 patients at high levels. Additionally, we identified common regulators of hub genes, including five miRNAs (miR-1-3p, miR-203a-3p, miR-210-3p, miR-603, and miR-124-3p) and one transcription factor (RELA). Collectively, our results highlighted the significant correlations between COVID-19 and VTE and pinpointed TP53I3 and SLPI as hub genes that potentially link the severity of both conditions. The hub genes and their common regulators might present an opportunity for the simultaneous treatment of these two diseases.

Potential bioactive compound and hub gene identification of endometrial carcinoma using systems biology

Endometrial carcinoma is a frequent cancer of the female genital tract. Endometrial carcinoma accounts for 97% of all uterine malignancies and 3 % of sarcomas that develop from the endometrium’s glands. Endometrial cancer is frequently found in its early stages since most women quickly report postmenopausal vaginal hemorrhage. The need for more advanced medications to improve survival in such situations is still unfulfilled. As a result, there is growing interest in employing an herbal treatment to treat endometriosis, which seems to be an effective strategy. We have discovered a few unintended targets (ligands) in our investigation that are active components of common therapeutic herbs. The differentially expressed genes (DEG - target protein) for endometrial cancer were found using the NCBI and CIViC databases. In our investigation, the protein used for docking and simulation was PDB ID: 3THW. Using the Cytoscape server, the gene-encoding protein network has been identified. It was discovered that the Protein 3THW's binding energy to the bioactive substance (Asarone) was −7.15 Kcal/mol. It was discovered that the crucial interacting amino acid residues were ILE648, PHE650, ILE651, VAL802, TYR815, VAL817. The properties of the pharmaceutical target are further investigated by employing a molecular simulation study for 100 ns with NAMD software. Low RMSD and SASA (Solvent accessible surface area), high RMSF, High hydrogen bonds, between Asarone and MSH2 demonstrated their potency as endometrial cancer inhibitor compounds. Based on these analyses we infer that the bioactive substances originating from medicinal plants may be an effective treatment for endometrial cancer. Communicated by Ramaswamy H. Sarma

Guideline directed medical therapy induced nephrotoxicity in HFrEF patients; an insight to its mechanism

Guideline Directed Medical Therapy (GDMT) has been the standard pharmacotherapy for the treatment of Heart Failure patients with reduced Ejection Fraction (HFrEF) recommended by the European Society of Cardiology (ESC). However, patients on GDMT are likely to possess nephrotoxicity as an adverse effect. We utilized multiple system biology tools like ADVER-Pred, gene enrichment analysis, molecular docking, molecular dynamic simulations, and MMPBSA analysis to predict a possible molecular mechanism of how selected combinations of GDMT may cause nephrotoxicity. As per the ACC/AHA/ESC guidelines, we categorized the drugs as category 1 including β-blockers (BB), angiotensin receptor blockers (ARB), and sodium-glucose cotransporter-2 inhibitors (SGLT2I), category 2 includes BB’s, SGLT2I, and angiotensin receptor-neprilysin inhibitors (ARNI), and category 3 includes BB’s, SGLT2I, and angiotensin-converting enzyme (ACE) inhibitors. Enrichment analysis predicted category 2 drugs to possess the highest number of proteins to be involved in the development of nephrotoxicity i.e. 79.41%. The targets HBA1, CBR1, ATG5, and SLC6A3 were the top hub genes with an edge count of 7 followed by GPX1 with an edge count of 6. Molecular docking studies revealed candesartan-SLC6A3 to possess the highest binding affinity of −10.2 kcal/mol. In addition, simulation studies displayed empagliflozin-CBR1 to possess the highest stability followed by candesartan-ATG5. A combination of β-blockers, ARBs, and SGLT2I are predicted to likely possess nephrotoxicity which may be due to the modulation of HBA1, CBR1, ATG5, and GPX1. In conclusion, candesartan and empagliflozin are most likely to cause nephrotoxicity via the modulation of HBA1, CBR1, ATG5, and GPX1. Communicated by Ramaswamy H. Sarma

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein’s crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models.

As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.

Network pharmacology-based prediction of efficacy and mechanism of Myrrha acting on Allergic Rhinitis

Objectives: Network pharmacology is an analysis method that explores drug-centered efficacy and mechanism by constructing a compound-target-disease network based on system biology, and is attracting attention as a methodology for studying herbal medicine that has the characteristics for multi-compound therapeutics. Thus, we investigated the potential functions and pathways of Myrrha on Allergic Rhinitis (AR) via network pharmacology analysis and molecular docking.Methods: Using public databases and PubChem database, compounds of Myrrha and their target genes were collected. The putative target genes of Myrrha and known target genes of AR were compared and found the correlation. Then, the network was constructed using STRING database, and functional enrichment analysis was conducted based on the Gene Ontology (GO) Biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways. Binding-Docking stimulation was performed using CB-Dock.Results: The result showed that total 3 compounds and 55 related genes were gathered from Myrrha. 33 genes were interacted with AR gene set, suggesting that the effects of Myrrha are closely related to AR. Target genes of Myrrha are considerably associated with various pathways including ‘Fc epsilon RI signaling pathway’ and ‘JAK-STAT signaling pathway’. As a result of blinding docking, AKT1, which is involved in both mechanisms, had high binding energies for abietic acid and dehydroabietic acid, which are components of Myrrha.Conclusion: Through a network pharmacological method, Myrrha was predicted to have high relevance with AR by regulating AKT1. This study could be used as a basis for studying therapeutic effects of Myrrha on AR.

Metabolomics-driven investigation of plant defense response against pest and pathogen attack.

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Prediction of Essential Proteins of Klebsiella pneumoniae using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery.

Klebsiella pneumoniae is an opportunistic multidrug-resistant bacterial pathogen responsible for various health care-associated infections. The prediction of proteins that are essential for the survival of bacterial pathogens can greatly facilitate the drug development and discovery pipeline toward target identification. To this end, the present study reports a comprehensive computational approach integrating bioinformatics and systems biology-based methods to identify essential proteins of K. pneumoniae involved in vital processes. From the proteome of this pathogen, we predicted a total of 854 essential proteins based on sequence, protein-protein interaction (PPI) and genome-scale metabolic model methods. These predicted essential proteins are involved in vital processes for cellular regulation such as translation, metabolism, and biosynthesis of essential factors, among others. Cluster analysis of the PPI network revealed the highly connected modules involved in the basic functionality of the organism. Further, the predicted consensus set of essential proteins of K. pneumoniae was evaluated by comparing them with existing resources (NetGenes and PATHOgenex) and literature. The findings of this study offer guidance toward understanding cell functionality, thereby facilitating the understanding of pathogen systems and providing a way forward to shortlist potential therapeutic candidates for developing novel antimicrobial agents against K. pneumoniae. In addition, the research strategy presented herein is a fusion of sequence and systems biology-based approaches that offers prospects as a model to predict essential proteins for other pathogens.

Spectralomics – Towards a holistic adaptation of label free spectroscopy

Vibrational spectroscopy, largely based on infrared absorption and Raman scattering techniques, is much vaunted as a label free approach, delivering a high content, holistic characterisation of a sample, with demonstrable applications in a broad range of fields, from process analytical technologies and preclinical drug screening, to disease diagnostics, therapeutics, prognostics and personalised medicine. However, in the analysis of such complex systems, a trend has emerged in which spectral analysis is reduced to the identification of individual peaks, based on reference tables of assignments derived from literature, which are then interpreted as biomarkers. More sophisticated analysis attempts to unmix the spectrum of the complex mixture into constituent components, which are then used to characterise the biochemistry of a sample and changes to it, in terms of its constituent components. Data mining the spectra, and in particular change due to kinetic processes, remains a challenge, and it is proposed that the rate of temporal evolution of the combination spectrum can be used in itself as a label by which to guide the spectral analysis. Ultimately, it is argued that the true potential of label free spectroscopy is best harnessed in a truly “spectralomic” approach, by which the spectral signature of an “event”, such as drug intercalation in the DNA of the nucleus of a cell, or a key stage of a cellular pathway such as oxidative stress, is presented. It is envisioned that, in the future, such Spectralomics pathway analysis will be fully integrated with similar omics approaches, potentially ultimately through deep learning algorithms, and underpinned by systems biology kinetic models, to provide a living human cell atlas, describing the function and dysfunction of organism at a cellular level, as the basis for improved healthcare.

Deciphering Breast Cancer Metastasis Cascade: A Systems Biology Approach Integrating Transcriptome and Interactome Insights for Target Discovery.

Breast cancer is the lead cause of cancer-related deaths among women globally. Breast cancer metastasis is a complex and still inadequately understood process and a key dimension of mortality attendant to breast cancer. This study reports dysregulated genes across metastatic stages and tissues, shedding light on their molecular interplay in disease pathogenesis and new possibilities for drug discovery. Comprehensive analyses of gene expression data from primary breast tumor, circulating tumor cells, and distant metastatic sites in the brain, lung, liver, and bone were conducted. Genes dysregulated across multiple stages and tissues were identified as metastatic cascade genes, and are further classified based on functional associations with metastasis-related mechanisms. Their interactions with HUB genes in interactome networks were scrutinized, followed by pathway enrichment analysis. Validation for their potential as targets included assessments for survival, druggability, prognostic marker status, secretome annotation, protein expression, and cell type marker association. Results displayed critical genes in the metastatic cascade and those specific to metastatic sites, revealing the involvement of the collagen degradation and assembly of collagen fibrils and other multimeric structure pathways in driving metastasis. Notably, pivotal cascade genes FABP4, CXCL12, APOD, and IGF1 emerged with high metastatic potential, linked to significant druggability and survival scores, establishing them as potential molecular targets. The significance of this research lies in its potential to uncover novel biomarkers for early detection, therapeutic targets, and a deeper understanding of the molecular mechanisms underpinning the metastatic cascade in breast cancer, and with an eye to precision/personalized medicine.

Holistic exploration of CHGA and hsa-miR-137 in colorectal cancer via multi-omic data Integration

Colorectal cancer (CRC) ranks among the most widespread malignancies globally, with early detection significantly influencing prognosis. Employing a systems biology approach, we aimed to unravel the intricate mRNA-miRNA network linked to CRC pathogenesis, potentially yielding diagnostic biomarkers. Through an integrative analysis of microarray, Bulk RNA-seq, and single-cell RNA-seq data, we explored CRC-related transcriptomes comprehensively. Differential gene expression analysis uncovered crucial genes, while Weighted Gene Co-expression Network Analysis (WGCNA) identified key modules closely linked to CRC. Remarkably, CRC manifested its strongest correlation with the turquoise module, signifying its pivotal role. From the cohort of genes showing high Gene Significance (GS) and Module Membership (MM), and Differential Expression Genes (DEGs), we highlighted the downregulated Chromogranin A (CHGA) as a notable hub gene in CRC. This finding was corroborated by the Human Protein Atlas database, which illustrated decreased CHGA expression in CRC tissues. Additionally, CHGA displayed elevated expression in primary versus metastatic cell lines, as evidenced by the CCLE database. Subsequent RT-qPCR validation substantiated the marked downregulation of CHGA in CRC tissues, reinforcing the significance of our differential expression analysis. Analyzing the Space-Time Gut Cell Atlas dataset underscored specific CHGA expression in epithelial cell subclusters, a trend persisting across developmental stages. Furthermore, our scrutiny of colon and small intestine Enteroendocrine cells uncovered distinct CHGA expression patterns, accentuating its role in CRC pathogenesis. Utilizing the WGCNA algorithm and TargetScan database, we validated the downregulation of hsa-miR-137 in CRC, and integrated assessment highlighted its interplay with CHGA. Our findings advocate hsa-miR-137 and CHGA as promising CRC biomarkers, offering valuable insights into diagnosis and prognosis. Despite proteomic analysis yielding no direct correlation, our multifaceted approach contributes comprehensive understanding of CRC's intricate regulatory mechanisms. In conclusion, this study advances hsa-miR-137 and CHGA as promising CRC biomarkers through an integrated analysis of diverse datasets and network interactions.

Tyr352 as a Predominant Phosphosite in the Understudied Kinase and Molecular Target, HIPK1: Implications for Cancer Therapy.

Homeodomain-interacting protein kinase 1 (HIPK1) is majorly found in the nucleoplasm. HIPK1 is associated with cell proliferation, tumor necrosis factor-mediated cellular apoptosis, transcription regulation, and DNA damage response, and thought to play significant roles in health and common diseases such as cancer. Despite this, HIPK1 remains an understudied molecular target. In the present study, based on a systematic screening and mapping approach, we assembled 424 qualitative and 44 quantitative phosphoproteome datasets with 15 phosphosites in HIPK1 reported across multiple studies. These HIPK1 phosphosites were not currently attributed to any functions. Among them, Tyr352 within the kinase domain was identified as the predominant phosphosite modulated in 22 differential datasets. To analyze the functional association of HIPK1 Tyr352, we first employed a stringent criterion to derive its positively and negatively correlated protein phosphosites. Subsequently, we categorized the correlated phosphosites in known interactors, known/predicted kinases, and substrates of HIPK1, for their prioritized validation. Bioinformatics analysis identified their significant association with biological processes such as the regulation of RNA splicing, DNA-templated transcription, and cellular metabolic processes. HIPK1 Tyr352 was also identified to be upregulated in Her2+ cell lines and a subset of pancreatic and cholangiocarcinoma tissues. These data and the systems biology approach undertaken in the present study serve as a platform to explore the functional role of other phosphosites in HIPK1, and by extension, inform cancer drug discovery and oncotherapy innovation. In all, this study highlights the comprehensive phosphosite map of HIPK1 kinase and the first of its kind phosphosite-centric analysis of HIPK1 kinase based on global-level phosphoproteomics datasets derived from human cellular differential experiments across distinct experimental conditions.

Molecular physiology of Antarctic diatom natural assemblages and bloom event reveal insights into strategies contributing to their ecological success.

In the Southern Ocean, phytoplankton must cope with harsh environmental conditions such as low light and growth-limiting concentrations of the micronutrient iron. Using metratranscriptomics, we assessed the influence of oceanographic variables on the diversity of the phytoplankton community composition and on the metabolic strategies of diatoms along the Western Antarctic Peninsula, a region undergoing rapid climate change. We found that cross-shelf differences in oceanographic parameters such as temperature and variable nutrient concentrations account for most of the differences in phytoplankton community composition and metabolism. We opportunistically characterized the metabolic underpinnings of a large bloom of the centric diatom Actinocyclus in coastal waters of the WAP. Our results indicate that physicochemical differences from onshore to offshore are stronger than between southern and northern regions of the WAP; however, these trends could change in the future, resulting in poleward shifts in functional differences in diatom communities and phytoplankton blooms.

Simultaneous dual-region two-photon imaging of biological dynamics spanning over 9  mm in vivo

Biodynamical processes, especially in system biology, that occur far apart in space may be highly correlated. To study such biodynamics, simultaneous imaging over a large span at high spatio-temporal resolutions is highly desired. For example, large-scale recording of neural network activities over various brain regions is indispensable in neuroscience. However, limited by the field-of-view (FoV) of conventional microscopes, simultaneous recording of laterally distant regions at high spatio-temporal resolutions is highly challenging. Here, we propose to extend the distance of simultaneous recording regions with a custom micro-mirror unit, taking advantage of the long working distance of the objective and spatio-temporal multiplexing. We demonstrate simultaneous dual-region two-photon imaging, spanning as large as 9 mm, which is 4 times larger than the nominal FoV of the objective. We verify the system performance in in vivo imaging of neural activities and vascular dilations, simultaneously, at two regions in mouse brains as well as in spinal cords, respectively. The adoption of our proposed scheme will promote the study of systematic biology, such as system neuroscience and system immunology.

Comprehensive transcriptomic meta-analysis unveils new responsive genes to methyl jasmonate and ethylene in Catharanthusroseus

In Catharanthus roseus, vital plant hormones, namely methyl jasmonate (MeJA) and ethylene, serve as abiotic triggers, playing a crucial role in stimulating the production of specific secondary compounds with anticancer properties. Understanding how plants react to various stresses, stimuli, and the pathways involved in biosynthesis holds significant promise. The application of stressors like ethylene and MeJA induces the plant's defense mechanisms, leading to increased secondary metabolite production. To delve into the essential transcriptomic processes linked to hormonal responses, this study employed an integrated approach combining RNA-Seq data meta-analysis and system biology methodologies. Furthermore, the validity of the meta-analysis findings was confirmed using RT-qPCR. Within the meta-analysis, 903 genes exhibited differential expression (DEGs) when comparing normal conditions to those of the treatment. Subsequent analysis, encompassing gene ontology, KEGG, TF, and motifs, revealed that these DEGs were actively engaged in multiple biological processes, particularly in responding to various stresses and stimuli. Additionally, these genes were notably enriched in diverse biosynthetic pathways, including those related to TIAs, housing valuable medicinal compounds found in this plant. Furthermore, by conducting co-expression network analysis, we identified hub genes within modules associated with stress response and the production of TIAs. Most genes linked to the biosynthesis pathway of TIAs clustered within three specific modules. Noteworthy hub genes, including Helicase ATP-binding domain, hbdA, and ALP1 genes within the blue, turquoise, and green module networks, are presumed to play a role in the TIAs pathway. These identified candidate genes hold potential for forthcoming genetic and metabolic engineering initiatives aimed at augmenting the production of secondary metabolites and medicinal compounds within C. roseus.

Screening out molecular pathways and prognostic biomarkers of ultraviolet-mediated melanoma through computational techniques.

Ultraviolet radiation causes skin cancer, but the exact mechanism by which it occurs and the most effective methods of intervention to prevent it are yet unknown. For this purpose, our study will use bioinformatics and systems biology approaches to discover potential biomarkers of skin cancer for early diagnosis and prevention of disease with applicable clinical treatments. This study compared gene expression and protein levels in ultraviolet-mediated cultured keratinocytes and adjacent normal skin tissue using RNA sequencing data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) database. Then, pathway analysis was employed with a selection of hub genes from the protein-protein interaction (PPI) network and the survival and expression profiles. Finally, potential clinical biomarkers were validated by receiver operating characteristic (ROC) curve analysis. We identified 32 shared differentially expressed genes (DEGs) by analyzing three different subsets of the GSE85443 dataset. Skin cancer development is related to the control of several DEGs through cyclin-dependent protein serine/threonine kinase activity, cell cycle regulation, and activation of the NIMA kinase pathways. The cytoHubba plugin in Cytoscape identified 12 hub genes from PPI; among these 3 DEGs, namely, AURKA, CDK4, and PLK1 were significantly associated with survival (P < 0.05) and highly expressed in skin cancer tissues. For validation purposes, ROC curve analysis indicated two biomarkers: AURKA (area under the curve (AUC) value = 0.8) and PLK1 (AUC value = 0.7), which were in an acceptable range. Further translational research, including clinical experiments, teratogenicity tests, and in-vitro or in-vivo studies, will be performed to evaluate the expression of these identified biomarkers regarding the prognosis of skin cancer patients.

System biology approach to delineate expressional difference in the blood mononuclear cells between healthy and Turner syndrome individuals

BackgroundTurner syndrome (TS) is a rare disorder associated either with complete or partial loss of one X chromosome in women. The information on the genotype–phenotype relationship in TS is inadequate. Comparing the healthy and Turner syndrome patients may help elucidate the mechanisms involved in TS pathophysiology. Gene expression differences between healthy and individuals with Turner syndrome were characterized using the systems-biology approach of weighted gene coexpression network analysis (WGCNA) on 182 microarray peripheral mononuclear blood samples (PBMC).ResultsThe coexpression networks of healthy and TS had scale-free topology that ensures network robustness. In the process, five modules were preserved between healthy and TS, which carry several genes common in each module. Two of them, SMCHD1 and PGK1, have already been reported to be involved in TS. Previously reported genes of TS, specifically, PTPN22, RPS4X, CSF2RA, and TIMP1, were missing in their respective modules. Dysfunction, differential expression, or absence of these genes could lead to a progressive disruption of molecular pathways leading to the pathophysiology of TS. Indeed, we observed a significant difference in the functions of these modules when compared within and across the healthy and TS samples. We identified four clusters in the PPI network constructed from the top 15 KME enriched in significant functions.ConclusionOverall, our work highlights the potential molecular functions, pathways, and molecular targets of TS that can be exploited therapeutically in the human healthcare system.

A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy

High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day machine learning and deep learning algorithms, this is widely impeded by the lack of suitable datasets. To address this, here we present a purpose-created open dataset of high-content microscopy sample preparation artefact. It consists of high-content microscopy of laboratory dust titrated on fixed cell culture specimens imaged with fluorescence filters covering the complete spectral range. To ensure this dataset is suitable for supervised machine learning tasks like image classification or segmentation we propose rule-based annotation strategies on categorical and pixel levels. We demonstrate the applicability of our dataset for deep learning by training a convolutional-neural-network-based classifier.

The landscape of transcription factor promoter activity during vegetative development in Marchantia.

Transcription factors (TFs) are essential for the regulation of gene expression and cell fate determination. Characterising the transcriptional activity of TF genes in space and time is a critical step towards understanding complex biological systems. The vegetative gametophyte meristems of bryophytes share some characteristics with the shoot apical meristems of flowering plants. However, the identity and expression profiles of TFs associated with gametophyte organization are largely unknown. With only ∼450 putative TF genes, Marchantia (Marchantia polymorpha) is an outstanding model system for plant systems biology. We have generated a near-complete collection of promoter elements derived from Marchantia TF genes. We experimentally tested reporter fusions for all the TF promoters in the collection and systematically analysed expression patterns in Marchantia gemmae. This allowed us to build a map of expression domains in early vegetative development and identify a set of TF-derived promoters that are active in the stem cell zone. The cell markers provide additional tools and insight into the dynamic regulation of the gametophytic meristem and its evolution. In addition, we provide an online database of expression patterns for all promoters in the collection. We expect that these promoter elements will be useful for cell-type-specific expression, synthetic biology applications, and functional genomics.

A pilot investigation of the association between HIV-1 Vpr amino acid sequence diversity and the tryptophan-kynurenine pathway as a potential mechanism for neurocognitive impairment

HIV infection compromises both the peripheral and central immune systems due to its pathogenic and neuropathogenic features. The mechanisms driving HIV-1 pathogenesis and neuropathogenesis involve a series of events, including metabolic dysregulation. Furthermore, HIV-subtype-specific variations, particularly alterations in the amino acid sequences of key viral proteins, are known to influence the severity of clinical outcomes in people living with HIV. However, the impact of amino acid sequence variations in specific viral proteins, such as Viral protein R (Vpr), on metabolites within the Tryptophan (Trp)-kynurenine (Kyn) pathway in people living with HIV remains unclear. Our research aimed to explore the relationship between variations in the Vpr amino acid sequence (specifically at positions 22, 41, 45, and 55, as these have been previously linked to neurocognitive function) and peripheral Trp-Kyn metabolites. Additionally, we sought to clarify the systems biology of Vpr sequence variation by examining the link between Trp-Kyn metabolism and peripheral inflammation, as a neuropathogenic mechanism. In this preliminary study, we analyzed a unique cohort of thirty-two (n = 32) South African cART naïve people living with HIV. We employed Sanger sequencing to ascertain blood-derived Vpr amino acid sequence variations and a targeted LC-MS/MS metabolomics platform to assess Trp-Kyn metabolites, such as Trp, Kyn, kynurenic acid (KA), and quinolinic acid (QUIN). Particle-enhanced turbidimetric assay and Enzyme-linked immunosorbent assays were used to measure immune markers, hsCRP, IL-6, suPAR, NGAL and sCD163. After applying Bonferroni corrections (p =.05/3) and adjusting for covariates (age and sex), only the Vpr G41 and A55 groups was nearing significance for higher levels of QUIN compared to the Vpr S41 and T55 groups, respectively (all p =.023). Multiple regression results revealed that Vpr amino acid variations at position 41 (adj R2 = 0.049, β = 0.505; p =.023), and 55 (adj R2 = 0.126, β = 0.444; p =.023) displayed significant associations with QUIN after adjusting for age and sex. Lastly, the higher QUIN levels observed in the Vpr G41 group were found to be correlated with suPAR (r =.588, p =.005). These results collectively underscore the importance of specific Vpr amino acid substitutions in influencing QUIN and inflammation (specifically suPAR levels), potentially contributing to our understanding of their roles in the pathogenesis and neuropathogenesis of HIV-1.