KEGG for representation and analysis of molecular networks involving diseases and drugs

Genome-wide investigations reveal the population structure and selection signatures of Nigerian cattle adaptation in the sub-Saharan tropics

Background

Cattle are considered to be the most desirable livestock by small scale farmers. In Africa, although comprehensive genomic studies have been carried out on cattle, the genetic variations in indigenous cattle from Nigeria have not been fully explored. In this study, genome-wide analysis based on genotyping-by-sequencing (GBS) of 193 Nigerian cattle was used to reveal new insights on the history of West African cattle and their adaptation to the tropical African environment, particularly in sub-Saharan region. 

Results

The GBS data were evaluated against whole-genome sequencing (WGS) data and high rate of variant concordance between the two platforms was evident with high correlated genetic distance matrices genotyped by both methods suggestive of the reliability of GBS applicability in population genetics. The genetic structure of Nigerian cattle was observed to be homogenous and unique from other African cattle populations. Selection analysis for the genomic regions harboring imprints of adaptation revealed genes associated with immune responses, growth and reproduction, efficiency of feeds utilization, and heat tolerance. Our findings depict potential convergent adaptation between African cattle, dogs and humans with adaptive genes SPRY2 and ITGB1BP1 possibly involved in common physiological activities.

Conclusion

The study presents unique genetic patterns of Nigerian cattle which provide new insights on the history of cattle in West Africa based on their population structure and the possibility of parallel adaptation between African cattle, dogs and humans in Africa which require further investigations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08512-w.

Global Gene Expression and Docking Profiling of COVID-19 Infection

Coronavirus is an enclosed positive-sense RNA virus with club-like spikes protruding from its surface that causes acute respiratory infections in humans. Because it is considered a member of the complex pathogen group, it has been found to infect different host species and cause a variety of diseases. So far, it has been discovered that it may affect the immune, infection, and inflammatory systems, leading to the hypothesis that the immune and inflammatory systems (signaling pathways and components) fail to control infection, opening the door to look for potential targets primarily in these systems. The study’s main purpose is to identify highly overexpressed genes and their functional implications as a result of COVID-19 infection, as well as to investigate probable infections, inflammation, and immune systems to better understand the impact of coronavirus infection. We explored the genes and pathways mostly linked with infection, inflammation, and the immune systems using the datasets available for COVID-19 infection gene expression compendium. NFKBIA, FN1, FAP, KANK4, COMP, FAM101B, COL1A2, ANKRD1, TAGLN, SPARC, ADAM19, OLFM4, CXCL10/11, OASL, FOS, APOBEC3A, IFI44L, IFI27, IFIT1, RSAD2, NDUFS1, SRSF6, HECTD1, CBX3, and DDX17 are among the genes that may be impacted by infection, according to our findings. The functional changes are mainly associated with these pathways TNF, cytokine, NF—kB, TLR, TCR, BCR, Foxo, and TGF signaling pathways are among them and there are additional pathways such as hippo signaling, apoptosis, estrogen signaling, regulating pluropotency of stem cells, ErbB, Wnt, p53, cAMP, MAPK, PI3K—AKT, oxidative phosphorylation, protein processing in endoplasmic reticulum, prolactin signaling, adipocytokine, neurotrophine signaling, and longevity regulating pathways. Moreover, we have also explored the potential herbal drug (apigenin, quercetin, and resveratrol) targets for the top-rated genes based on the overall analysis where we observe that quercetin and resveratrol as most effective.

Exploring the Potential Antidepressant Mechanisms of Pinellia by Using the Network Pharmacology and Molecular Docking

About 350 million people worldwide suffered from depression, but less than half of the patients received effective and regular treatments. Traditional Chinese Medicine (TCM) such as pinellia has been proven effective for antidepressant treatment with fewer side effects. However, the exact mechanisms remain unclear. Herein, we use the methods of network pharmacology and molecular docking to analyze the effective monomer components of pinellia and reveal the involved signaling pathways to produce antidepressant effects. TCMSP, BATMAN-TCM, and TCMID databases were utilized to analyze the bioactive ingredients and target genes derived from pinellia via the screening the molecular weight (MW), oral bioavailability (OB), blood-brain barrier (BBB) and drug similarity (DL). OMIM, TTD, DisGeNET, GeneCards and DrugBank databases were used to obtain key genes of depression. Then, the networks of protein-protein interaction (PPI) and "medicine-ingredients-targets-pathways" were built. The target signaling pathways were enriched by GO and KEGG by using R language. Furthermore, bioactive ingredients binding of the targets were verified by molecular docking. Nine active monomer ingredients and 96 pivotal gene targets were selected from pinellia. 10,124 disease genes and 87 drug-disease intersecting genes were verified. GO analysis proposed that the receptor activity of neurotransmitter, postsynaptic neurotransmitter, G protein-coupled neurotransmitter, and acetylcholine through the postsynaptic membrane could be modulated by pinellia. KEGG pathway analysis revealed that pinellia influenced depression-related neural tissue interaction, cholinergic synapse, serotonin activated synapse and calcium signaling pathway. Besides, the reliability and accuracy of results obtained from the indirect network pharmacology were validated by molecular docking. The bioactive components of pinellia made significant antidepressant effects by regulating the key target genes/proteins in the pathophysiology of depression.

Infection by phloem-limited phytoplasma affects mineral nutrient homeostasis in tomato leaf tissues

Phytoplasmas are sieve-elements restricted wall-less, pleomorphic pathogenic microorganisms causing devastating damage to over 700 plant species worldwide. The invasion of sieve elements by phytoplasmas has several consequences on nutrient transport and metabolism, anyway studies about changes of the mineral-nutrient profile following phytoplasma infections are scarce and offer contrasting results. Here, we examined changes in macro- and micronutrient concentration in tomato plant upon 'Candidatus Phytoplasma solani' infection. To investigate possible effects of 'Ca. P. solani' infection on mineral element allocation, the mineral elements were separately analysed in leaf midrib, leaf lamina and root. Moreover, we focused our analysis on the transcriptional regulation of genes encoding trans-membrane transporters of mineral nutrients. To this aim, a manually curated inventory of differentially expressed genes encoding transporters in tomato leaf midribs was mined from the transcriptional profile of healthy and infected tomato leaf midribs. Results highlighted changes in ion homeostasis in the host plant, and significant modulations at transcriptional level of genes encoding ion transporters and channels.

A Genome-Scale Metabolic Model of Methanoperedens nitroreducens: Assessing Bioenergetics and Thermodynamic Feasibility

Methane is an abundant low-carbon fuel that provides a valuable energy resource, but it is also a potent greenhouse gas. Therefore, anaerobic oxidation of methane (AOM) is an essential process with central features in controlling the carbon cycle. Candidatus 'Methanoperedens nitroreducens' (M. nitroreducens) is a recently discovered methanotrophic archaeon capable of performing AOM via a reverse methanogenesis pathway utilizing nitrate as the terminal electron acceptor. Recently, reverse methanogenic pathways and energy metabolism among anaerobic methane-oxidizing archaea (ANME) have gained significant interest. However, the energetics and the mechanism for electron transport in nitrate-dependent AOM performed by M. nitroreducens is unclear. This paper presents a genome-scale metabolic model of M. nitroreducens, iMN22HE, which contains 813 reactions and 684 metabolites. The model describes its cellular metabolism and can quantitatively predict its growth phenotypes. The essentiality of the cytoplasmic heterodisulfide reductase HdrABC in the reverse methanogenesis pathway is examined by modeling the electron transfer direction and the specific energy-coupling mechanism. Furthermore, based on better understanding electron transport by modeling, a new energy transfer mechanism is suggested. The new mechanism involves reactions capable of driving the endergonic reactions in nitrate-dependent AOM, including the step reactions in reverse canonical methanogenesis and the novel electron-confurcating reaction HdrABC. The genome metabolic model not only provides an in silico tool for understanding the fundamental metabolism of ANME but also helps to better understand the reverse methanogenesis energetics and its thermodynamic feasibility.

Metagenomic Insights Into the Changes of Antibiotic Resistance and Pathogenicity Factor Pools Upon Thermophilic Composting of Human Excreta

In times of climate change, practicing a form of sustainable, climate-resilient and productive agriculture is of primordial importance. Compost could be one form of sustainable fertilizer, which is increasing humus, water holding capacity, and nutrient contents of soils. It could thereby strengthen agriculture toward the adverse effects of climate change, especially when additionally combined with biochar. To get access to sufficient amounts of suitable materials for composting, resources, which are currently treated as waste, such as human excreta, could be a promising option. However, the safety of the produced compost regarding human pathogens, pharmaceuticals (like antibiotics) and related resistance genes must be considered. In this context, we have investigated the effect of 140- and 154-days of thermophilic composting on the hygienization of human excreta and saw dust from dry toilets together with straw and green cuttings with and without addition of biochar. Compost samples were taken at the beginning and end of the composting process and metagenomic analysis was conducted to assess the fate of antibiotic resistance genes (ARGs) and pathogenicity factors of the microbial community over composting. Potential ARGs conferring resistance to major classes of antibiotics, such as beta-lactam antibiotics, vancomycin, the MLS_B group, aminoglycosides, tetracyclines and quinolones were detected in all samples. However, relative abundance of ARGs decreased from the beginning to the end of composting. This trend was also found for genes encoding type III, type IV, and type VI secretion systems, that are involved in pathogenicity, protein effector transport into eukaryotic cells and horizontal gene transfer between bacteria, respectively. The results suggest that the occurrence of potentially pathogenic microorganisms harboring ARGs declines during thermophilic composting. Nevertheless, ARG levels did not decline below the detection limit of quantitative PCR (qPCR). Thresholds for the usage of compost regarding acceptable resistance gene levels are yet to be evaluated and defined.

Decoding the Role of Astrocytes in the Entorhinal Cortex in Alzheimer’s Disease Using High-Dimensional Single-Nucleus RNA Sequencing Data and Next-Generation Knowledge Discovery Methodologies: Focus on Drugs and Natural Product Remedies for Dementia

Introduction: Alzheimer’s disease (AD) is a major cause of the development of cognitive decline and dementia. AD and associated dementias (ADRD) are the major contributors to the enormous burden of morbidity and mortality worldwide. To date, there are no robust therapies to alleviate or cure this debilitating disease. Most drug treatments focus on restoring the normal function of neurons and the cells that cause inflammation, such as microglia in the brain. However, the role of astrocytes, the brain’s housekeeping cells, in the development of AD and the initiation of dementia is still not well understood.

Objective: To decipher the role of astrocytes in the entorhinal cortex of AD patients using single nuclear RNA sequencing (snRNASeq) datasets from the Single Cell RNA-seq Database for Alzheimer’s Disease (scREAD). The datasets were originally derived from astrocytes, isolated from the entorhinal cortex of AD brain and healthy brain to decipher disease-specific signaling pathways as well as drugs and natural products that reverse AD-specific signatures in astrocytes.

Methods: We used snRNASeq datasets from the scREAD database originally derived from astrocytes isolated from the entorhinal cortex of AD and healthy brains from the Gene Expression Omnibus (GEO) (GSE138852 and GSE147528) and analyzed them using next-generation knowledge discovery (NGKD) platforms. scREAD is a user-friendly open-source interface available at https://bmbls.bmi.osumc.edu/scread/that enables more discovery-oriented strategies. snRNASeq data and metadata can also be visualized and downloaded via an interactive web application at adsn.ddnetbio.com. Differentially expressed genes (DEGs) for each snRNASeq dataset were analyzed using iPathwayGuide to compare and derive disease-specific pathways, gene ontologies, and in silico predictions of drugs and natural products that regulate AD -specific signatures in astrocytes. In addition, DEGs were analyzed using the L1000FWD and L1000CDS2 signature search programming interfaces (APIs) to identify additional drugs and natural products that mimic or reverse AD-specific gene signatures in astrocytes.

Results: We found that PI3K/AKT signaling, Wnt signaling, neuroactive ligand-receptor interaction pathways, neurodegeneration pathways, etc. were significantly impaired in astrocytes from the entorhinal cortex of AD patients. Biological processes such as glutamate receptor signaling pathway, regulation of synapse organization, cell-cell adhesion via plasma membrane adhesion molecules, and chylomicrons were negatively enriched in the astrocytes from the entorhinal cortex of AD patients. Gene sets involved in cellular components such as postsynaptic membrane, synaptic membrane, postsynapse, and synapse part were negatively enriched (p < 0.01). Moreover, molecular functions such as glutamate receptor activity, neurotransmitter receptor activity, and extracellular ligand-gated ion channels were negatively regulated in the astrocytes of the entorhinal cortex of AD patients (p < 0.01). Moreover, the application of NGKD platforms revealed that antirheumatic drugs, vitamin-E, emetine, narciclasine, cephaeline, trichostatin A, withaferin A, dasatinib, etc. can potentially reverse gene signatures associated with AD.

Conclusions: The present study highlights an innovative approach to use NGKD platforms to find unique disease-associated signaling pathways and specific synthetic drugs and natural products that can potentially reverse AD and ADRD-associated gene signatures.

Comparative Genome Analysis of ‘Candidatus Phytoplasma luffae’ Reveals the Influential Roles of Potential Mobile Units in Phytoplasma Evolution

Phytoplasmas are insect-transmitted plant pathogens that cause substantial losses in agriculture. In addition to economic impact, phytoplasmas induce distinct disease symptoms in infected plants, thus attracting attention for research on molecular plant-microbe interactions and plant developmental processes. Due to the difficulty of establishing an axenic culture of these bacteria, culture-independent genome characterization is a crucial tool for phytoplasma research. However, phytoplasma genomes have strong nucleotide composition biases and are repetitive, which make it challenging to produce complete assemblies. In this study, we utilized Illumina and Oxford Nanopore sequencing technologies to obtain the complete genome sequence of ‘Candidatus Phytoplasma luffae’ strain NCHU2019 that is associated with witches’ broom disease of loofah (Luffa aegyptiaca) in Taiwan. The fully assembled circular chromosome is 769 kb in size and is the first representative genome sequence of group 16SrVIII phytoplasmas. Comparative analysis with other phytoplasmas revealed that NCHU2019 has a remarkably repetitive genome, possessing a pair of 75 kb repeats and at least 13 potential mobile units (PMUs) that account for ∼25% of its chromosome. This level of genome repetitiveness is exceptional for bacteria, particularly among obligate pathogens with reduced genomes. Our genus-level analysis of PMUs demonstrated that these phytoplasma-specific mobile genetic elements can be classified into three major types that differ in gene organization and phylogenetic distribution. Notably, PMU abundance explains nearly 80% of the variance in phytoplasma genome sizes, a finding that provides a quantitative estimate for the importance of PMUs in phytoplasma genome variability. Finally, our investigation found that in addition to horizontal gene transfer, PMUs also contribute to intra-genomic duplications of effector genes, which may provide redundancy for subfunctionalization or neofunctionalization. Taken together, this work improves the taxon sampling for phytoplasma genome research and provides novel information regarding the roles of mobile genetic elements in phytoplasma evolution.

Deep neural network prediction of genome-wide transcriptome signatures - beyond the Black-box

Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). We find that the expression of 1600 TFs can explain >95% of the variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an over-representation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the dysregulation of the target genes (rho = 0.61, P < 10⁻²¹⁶). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. We demonstrate a methodology for constructing an interpretable neural network predictor, where analyses of the predictors identified key TFs that were inducing transcriptional changes during disease.

A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning

Drug-drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug-drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses

Background

Elucidating the modes of action (MoAs) of drugs and drug candidate compounds is critical for guiding translation from drug discovery to clinical application. Despite the development of several data-driven approaches for predicting chemical–disease associations, the molecular cues that organize the epigenetic landscape of drug responses remain poorly understood.

Results

With the use of a computational method, we attempted to elucidate the epigenetic landscape of drug responses, in terms of transcription factors (TFs), through large-scale ChIP-seq data analyses. In the algorithm, we systematically identified TFs that regulate the expression of chemically induced genes by integrating transcriptome data from chemical induction experiments and almost all publicly available ChIP-seq data (consisting of 13,558 experiments). By relating the resultant chemical–TF associations to a repository of associated proteins for a wide range of diseases, we made a comprehensive prediction of chemical–TF–disease associations, which could then be used to account for drug MoAs. Using this approach, we predicted that: (1) cisplatin promotes the anti-tumor activity of TP53 family members but suppresses the cancer-inducing function of MYCs; (2) inhibition of RELA and E2F1 is pivotal for leflunomide to exhibit antiproliferative activity; and (3) CHD8 mediates valproic acid-induced autism.

Conclusions

Our proposed approach has the potential to elucidate the MoAs for both approved drugs and candidate compounds from an epigenetic perspective, thereby revealing new therapeutic targets, and to guide the discovery of unexpected therapeutic effects, side effects, and novel targets and actions.

RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection

Some studies reported that genomic RNA of SARS-CoV-2 can absorb a few host miRNAs that regulate immune-related genes and then deprive their function. In this perspective, we conjecture that the absorption of the SARS-CoV-2 genome to host miRNAs is not a coincidence, which may be an indispensable approach leading to viral survival and development in host. In our study, we collected five datasets of miRNAs that were predicted to interact with the genome of SARS-CoV-2. The targets of these miRNAs in the five groups were consistently enriched immune-related pathways and virus-infectious diseases. Interestingly, the five datasets shared no one miRNA but their targets shared 168 genes. The signaling pathway enrichment of 168 shared targets implied an unbalanced immune response that the most of interleukin signaling pathways and none of the interferon signaling pathways were significantly different. Protein-protein interaction (PPI) network using the shared targets showed that PPI pairs, including IL6-IL6R, were related to the process of SARS-CoV-2 infection and pathogenesis. In addition, we found that SARS-CoV-2 absorption to host miRNA could benefit two popular mutant strains for more infectivity and pathogenicity. Conclusively, our results suggest that genomic RNA absorption to host miRNAs may be a vital approach by which SARS-CoV-2 disturbs the host immune system and infects host cells.

CircleBase: an integrated resource and analysis platform for human eccDNAs

Rapid advances in high-throughput sequencing technologies have led to the discovery of thousands of extrachromosomal circular DNAs (eccDNAs) in the human genome. Loss-of-function experiments are difficult to conduct on circular and linear chromosomes, as they usually overlap. Hence, it is challenging to interpret the molecular functions of eccDNAs. Here, we present CircleBase (http://circlebase.maolab.org), an integrated resource and analysis platform used to curate and interpret eccDNAs in multiple cell types. CircleBase identifies putative functional eccDNAs by incorporating sequencing datasets, computational predictions, and manual annotations. It classifies them into six sections including targeting genes, epigenetic regulations, regulatory elements, chromatin accessibility, chromatin interactions, and genetic variants. The eccDNA targeting and regulatory networks are displayed by informative visualization tools and then prioritized. Functional enrichment analyses revealed that the top-ranked cancer cell eccDNAs were enriched in oncogenic pathways such as the Ras and PI3K-Akt signaling pathways. In contrast, eccDNAs from healthy individuals were not significantly enriched. CircleBase provides a user-friendly interface for searching, browsing, and analyzing eccDNAs in various cell/tissue types. Thus, it is useful to screen for potential functional eccDNAs and interpret their molecular mechanisms in human cancers and other diseases.

Key genes associated with prognosis and metastasis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a tumor that frequently shows the hematogenous pathway and tends to be resistant to radiotherapy and chemotherapy. However, the exact mechanism of ccRCC metastasis remains unknown.

METHODS

Differentially expressed genes (DEGs) of three gene expression profiles (GSE85258, GSE105288 and GSE22541) downloaded from the Gene Expression Omnibus (GEO) database were analyzed by GEO2R analysis, and co-expressed DEGs among the datasets were identified using a Venn drawing tool. The co-expressed DEGs were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and hub genes were determined based on the protein-protein interaction network established by STRING. After survival analysis performed on UALCAN website, possible key genes were selected and verified in ccRCC cell lines and ccRCC tissues (n = 44). Statistical analysis was conducted using GraphPad Prism (Version 8.1.1).

RESULTS

A total of 104 co-expressed DEGs were identified in the three datasets. Pathway analysis revealed that these genes were enriched in the extracellular matrix (ECM)-receptor interaction, protein digestion and absorption and focal adhesion. Survival analysis on 17 hub genes revealed that four key genes with a significant impact on survival: procollagen C-endopeptidase enhancer (PCOLCE), prolyl 4-hydroxylase subunit beta (P4HB), collagen type VI alpha 2 (COL6A2) and collagen type VI alpha 3 (COL6A3). Patients with higher expression of these key genes had worse survival than those with lower expression. In vitro experiments revealed that the mRNA expression levels of PCOLCE, P4HB and COL6A2 were three times higher and that of COL6A3 mRNA was 16 times higher in the metastatic ccRCC cell line Caki-1 than the corresponding primary cell line Caki-2. Immunohistochemistry revealed higher expression of the proteins encoded by these four genes in metastatic ccRCC compared with tumors from the corresponding primary sites, with statistical significance.

CONCLUSION

PCOLCE, P4HB, COL6A2 and COL6A3 are upregulated in metastatic ccRCC and might be related to poor prognosis and distant metastases.

Mechanisms of Action of a Herbal Formula Huangqi Guizhi Wuwu Tang for the Management of Post-Stroke Related Numbness and Weakness: A Computational Molecular Docking Study

Stroke-related numbness and weakness (SRNW) are resultant symptoms of post-stroke sufferers. Existing research has supported the use of Huangqi Guizhi Wuwu Tang (HGWT) particularly for SRNW; however, their mechanisms of action have not been fully elucidated. Therefore, this study aimed to investigate the mechanisms of action of HGWT components targeting SRNW-related proteins through a computational molecular docking approach. Target proteins associated with SRNW were identified through DrugBank database and Open Targets database. Chemical compounds from each herb of HGWT were identified from the Traditional Chinese Medicine Systems Pharmacology and Analysis Platform (TCMSP). Autodock Vina was utilized and the cut-off criterion applied for protein-ligand complexes was a binding affinity score of ≤ -9.5 kcal/mol; selected protein-ligand complexes were identified using 3D and 2D structural analyses. The protein targets PDE5A and ESR1 have highlighted interactions with compounds (BS040, DZ006, DZ058, DZ118, and HQ066) which are the key molecules in the management of SRNW. PDE5A have bioactivity with the amino acid residues (Val230, Asn252, Gln133 and Thr166) throughout PDE5A-cGMP-PKG pathways which involved reduction in myofilament responsiveness. ESR1 were predicted to be critical active with site residue (Leu346, Glu419 and Leu387) and its proteoglycans pathway involving CD44v3/CD44 that activates rho-associated protein kinase 1 (ROCK1) and ankyrin increasing vascular smooth muscle. In conclusion, HGWT may provide therapeutic benefits through strong interactions between herbal compounds and target proteins of PDE5A and ESR1. Further experimental studies are needed to unequivocally support this result which can be valuable to increase the quality of life of post-stroke patients. Keywords Herbal medicine, Complementary and alternative medicine, Natural product, Post-stroke, Computational analysis.

Improved prognosis and evidence of enhanced immunogenicity in tumor and circulation of high-risk melanoma patients with unknown primary

BACKGROUND

Melanoma of unknown primary (MUP) represents a poorly understood group of patients both clinically and immunologically. We investigated differences in prognosis and candidate immune biomarkers in patients with unknown compared with those with known primary melanoma enrolled in the E1609 adjuvant trial that tested ipilimumab at 3 and 10 mg/kg vs high-dose interferon-alfa (HDI).

PATIENTS AND METHODS

MUP status was defined as initial presentation with cutaneous, nodal or distant metastasis without a known primary. Relapse-free survival (RFS) and overall survival (OS) rates were estimated by the Kaplan-Meier method. Stratified (by stage) log-rank test was used to compare RFS and OS by primary tumor status. Gene expression profiling (GEP) was performed on the tumor biopsies of a subset of patients. Similarly, peripheral blood samples were tested for candidate soluble and cellular immune biomarkers.

RESULTS

MUP cases represented 12.8% of the total population (N=1699) including 11.7% on the ipilimumab arms and 14.7% on the HDI arm. Stratifying by stage, RFS (p=0.001) and overall survival (OS) (p=0.009) showed outcomes significantly better for patients with unknown primary. The primary tumor status remained prognostically significant after adjusting for treatment and stage in multivariate Cox proportional hazards models. Including only ipilimumab-treated patients, RFS (p=0.005) and OS (p=0.023) were significantly better in favor of those with unknown primary. Among patients with GEP data (n=718; 102 MUP, 616 known), GEP identified pathways and genes related to autoimmunity, inflammation, immune cell infiltration and immune activation that were significantly enriched in the MUP tumors compared with known primaries. Further investigation into infiltrating immune cell types estimated significant enrichment with CD8 +and CD4+T cells, B cells and NK cells as well as significantly higher major histocompatibility complex (MHC)-I and MHC-II scores in MUP compared with known primary. Among patients tested for circulating biomarkers (n=321; 66 unknown and 255 known), patients with MUP had significantly higher circulating levels of IL-2R (p=0.04).

CONCLUSION

Patients with MUP and high-risk melanoma had significantly better prognosis and evidence of significantly enhanced immune activation within the TME and the circulation, supporting the designation of MUP as a distinct prognostic marker in patients with high-risk melanoma.

Sub-lethal exposure to malaria vector control pesticides causes alterations in liver metabolomics and behaviour of the African clawed frog (Xenopus laevis)

In this study we explore the sub-lethal effects of two malaria vector control pesticides, deltamethrin and dichlorodiphenyltrichloroethane (DDT), on Xenopus laevis by incorporating different levels of biological organisation. Pesticide accumulation in frog tissue was measured alongside liver metabolomics and individual swimming behaviour to assess whether changes presented at these different levels, and if such changes could be linked between levels. Results showed evidence of concentration dependent accumulation of DDT and its metabolites, but no measurable accumulation of deltamethrin in adult X. laevis after 96 h of exposure. Both DDT and deltamethrin were shown to cause alterations in the liver metabolome of X. laevis. We also showed that some of these changes can be enhanced in exposure to a mixture of these two pesticides. Initial behavioural responses recorded directly after exposure were seen in the form of decreased activity, less alterations between mobility states, and less time spent at the water surface. This response persisted after 96 h of exposure to a mixture of the two pesticides. This study shows that sub-lethal exposure to pesticides can alter the biochemical homeostasis of frogs with the potential to cascade onto behavioural and ecological levels in mixture exposure scenarios.

Predicting Drug-Drug Interactions Based on Integrated Similarity and Semi-Supervised Learning

A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause the major cause of adverse drug reactions and even result in the drug withdrawal from the market and the patient death. Therefore, identifying DDIs has become a key component of the drug development and disease treatment. In this study, we propose a novel method to predict DDIs based on the integrated similarity and semi-supervised learning (DDI-IS-SL). DDI-IS-SL integrates the drug chemical, biological and phenotype data to calculate the feature similarity of drugs with the cosine similarity method. The Gaussian Interaction Profile kernel similarity of drugs is also calculated based on known DDIs. A semi-supervised learning method (the Regularized Least Squares classifier) is used to calculate the interaction possibility scores of drug-drug pairs. In terms of the 5-fold cross validation, 10-fold cross validation and de novo drug validation, DDI-IS-SL can achieve the better prediction performance than other comparative methods. In addition, the average computation time of DDI-IS-SL is shorter than that of other comparative methods. Finally, case studies further demonstrate the performance of DDI-IS-SL in practical applications.

Existing Drug Repurposing for Glioblastoma to Discover Candidate Drugs as a New a Approach

Aims:

Repurposing of drugs has been hypothesized as a means of identifying novel treatment methods for certain diseases.

Background:

Glioblastoma (GB) is an aggressive type of human cancer; the most effective treatment for glioblastoma is chemotherapy, whereas, when repurposing drugs, a lot of time and money can be saved.

Objective:

Repurposing of the existing drug may be used to discover candidate drugs for individualized treatments of GB.

Method:

We used the bioinformatics method to obtain the candidate drugs. In addition, the drugs were verified by MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation experiments, as well as statistical analysis.

Result:

We obtained 4 candidate drugs suitable for the treatment of glioma, camptothecin, doxorubicin, daunorubicin and mitoxantrone, by the expression spectrum data IPAS algorithm analysis and drug-pathway connectivity analysis. These validation experiments showed that camptothecin was more effective in treating the GB, such as MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation.

Conclusion:

With regard to personalized treatment, this present study may be used to guide the research of new drugs via verification experiments and tumor formation. The present study also provides a guide to systematic, individualized drug discovery for complex diseases and may contribute to the future application of individualized treatments.

Progress towards machine learning reaction rate constants

Quantum and classical reaction rate constant calculations come at the cost of exploring potential energy surfaces. Due to the "curse of dimensionality", their evaluation quickly becomes unfeasible as the system size grows. Machine learning algorithms can accelerate the calculation of reaction rate constants by predicting them using low cost input features. In this perspective, we briefly introduce supervised machine learning algorithms in the context of reaction rate constant prediction. We discuss existing and recently created kinetic datasets and input feature representations as well as the use and design of machine learning algorithms to predict reaction rate constants or quantities required for their computation. Amongst these, we first describe the use of machine learning to predict activation, reaction, solvation and dissociation energies. We then look at the use of machine learning to predict reactive force field parameters, reaction rate constants as well as to help accelerate the search for minimum energy paths. Lastly, we provide an outlook on areas which have yet to be explored so as to improve and evaluate the use of machine learning algorithms for chemical reaction rate constants.

Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools

PURPOSE

Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial intelligence (AI), have been transferred to different application domains such as biomedicine.

METHODS

In this study, we conducted an investigative analysis via data-driven evaluation of potential hepatocellular carcinoma (HCC) therapeutics in the context of AI-assisted drug discovery/repurposing. First, we discussed basic concepts, computational approaches, databases, modeling approaches, and featurization techniques in drug discovery/repurposing. In the analysis part, we automatically integrated HCC-related biological entities such as genes/proteins, pathways, phenotypes, drugs/compounds, and other diseases with similar implications, and represented these heterogeneous relationships via a knowledge graph using the CROssBAR system.

RESULTS

Following the system-level evaluation and selection of critical genes/proteins and pathways to target, our deep learning-based drug/compound-target protein interaction predictors DEEPScreen and MDeePred have been employed for predicting new bioactive drugs and compounds for these critical targets. Finally, we embedded ligands of selected HCC-associated proteins which had a significant enrichment with the CROssBAR system into a 2-D space to identify and repurpose small molecule inhibitors as potential drug candidates based on their molecular similarities to known HCC drugs.

CONCLUSIONS

We expect that these series of data-driven analyses can be used as a roadmap to propose early-stage potential inhibitors (from database-scale sets of compounds) to both HCC and other complex diseases, which may subsequently be analyzed with more targeted in silico and experimental approaches.

Deciphering functional biomolecule potential of marine diatoms through complex network approach

Marine diatoms are unique reservoirs of bioactive compounds having enormous applications in therapeutics. But high-throughput screening methods are needed to elucidate the interaction between numerous biomolecules and their targets, facilitating rapid screening for novel drug molecules. So, in the present study chemical constituents were extracted from five marine diatoms using un-targeted metabolite profiling and in-silico virtual screening bioinformatics was employed to predict their bioactivity and molecular targets. A total of 17 chemical constituents out of 51 showed interactions with 76 protein targets associated with 213 pathways. Ingredient-target-pathway network revealed oleic acid, linoleic acid and cholest-5-en-3-ol as major active constituents. Core subnetwork and protein association network showed involvement of these compounds in key metabolic pathways related to cell signaling, cell growth and metabolism of xenobiotics. Thus, the present study for the first time revealed the main active ingredients and their associated pathways from marine diatoms using complex network approach.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients

Background

Myelodysplastic syndrome (MDS) is a group of hematological malignancies that may progress to acute myeloid leukemia (AML). Bioinformatics-based analysis of high-frequency mutation genes in MDS-related patients is still relatively rare, so we conducted our research to explore whether high-frequency mutation genes in MDS-related patients can play a reference role in clinical guidance and prognosis.

Methods

Next generation sequencing (NGS) technology was used to detect 32 mutations in 64 MDS-related patients. We classified the patients' genes and analyzed them by Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, protein-protein interaction (PPI) analysis, and then calculated the gene survival curve of high-frequency mutations.

Results

We discovered 32 mutant genes such as ASXL1, DNMT3A, KRAS, NRAS, TP53, SF3B1, and SRSF2. The overall survival (OS) of these genes decreased significantly after DNMT3A, ASXL1, RUNX1, and U2AF1 occurred mutation. These genes play a significant role in biological processes, not only in MDS but also in the occurrence and development of other diseases. Through retrospective analysis, genes associated with MDS-related diseases were identified, and their effects on the disease were predicted.

Conclusions

Thirty-two mutant genes were determined in MDS and when mutations occur in DNMT3A, ASXL1, RUNX1, and U2AF1, their survival time decreases significantly. This results providing a theoretical basis for clinical and scientific research and broadening the scope of research on MDS.

Beneficial effects of Dendrobium nobile Lindl. Alkaloids (DNLA) on anxiety and depression induced by chronic unpredictable stress in rats

Dendrobium nobile Lindl. alkaloid (DNLA) is effective against animal models of Alzheimer's disease. This study further examined its effect on anxiety and depression produced by chronic unpredictable stress (CUS). Rats were subjected to CUS for 42 days, followed by DNLA treatment (20 mg/kg/day, po) for 28 days. The behavioral tests, histopathology, neurotransmitters and RNA-Seq were examined. DNLA attenuated body weight loss and CUS-induced anxiety/depressive-like behaviors, as evidenced by the elevated-plus-maze test, open-field test and sucrose preference. DNLA alleviated neuronal damage and loss and increased Nissl bodies in the hippocampus CA2 region and cortex. DNLA decreased CUS-elevated 5-hydroxytryptamine, dopamine and monoamine oxidase and catechol-O-methyltransferase activities in the brain. DNLA attenuated HPA activation by decreasing adrenocorticotropic hormones and the expression of corticotropin-releasing hormone receptor-1, and increased the expression of glucocorticoid receptor in the brain. RNA-Seq revealed distinct gene expression patterns among groups. Gene ontology revealed the cell projection assembly, postsynapse and centrosome as top biological processes, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment showed the cAMP, cGMP-PKG, glutamatergic synapse and circadian as major pathways for DNLA effects. Using DESeq2, CUS modulated 1700 differentially expressed genes (DEGs), which were prevented or attenuated by DNLA. CUS-induced DEGs were highly correlated with the Gene Expression Omnibus (GEO) database for anxiety and depression and were ameliorated by DNLA. Taken together, DNLA attenuated anxiety/depression-like behavior and neuronal damage induced by CUS in rats. The mechanisms could be related to regulation of the monoamine neurotransmitters and the HPA axis, and modulation of gene expression in the hippocampus.

Machine learning approaches for drug combination therapies

Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.

Variimorphobacter saccharofermentans gen. nov., sp. nov., a new member of the family Lachnospiraceae, isolated from a maize-fed biogas fermenter

Strain MD1^T is an anaerobic, Gram-stain-negative bacterium isolated from a lab-scale biogas fermenter fed with maize silage. It has a rod-shaped morphology with peritrichously arranged appendages and forms long chains of cells and coccoid structures. The colonies of MD1^T were white, circular, slightly convex and had a smooth rim. The isolate is mesophilic, displaying growth between 25 and 45 °C with an optimum at 40 °C. It grew at pH values of pH 6.7-8.2 (optimum, pH 7.1) and tolerated the addition of up to 1.5% (w/v) NaCl to the medium. The main cellular fatty acids of MD1^T are C_14:0 DMA and C_16:0. Strain MD1^T fermented xylose, arabinose, glucose, galactose, cellobiose, maltose, maltodextrin10, lactose starch, and xylan, producing mainly 2-propanol and acetic acid. The genome of the organism has a total length of 4163427 bp with a G+C content of 38.5 mol%. The two closest relatives to MD1^T are Mobilitalea sibirica P3M-3^T and Anaerotaenia torta FH052^T with 96.44 or 95.8 % 16S rRNA gene sequence similarity and POCP values of 46.58 and 50.58%, respectively. As MD1^T showed saccharolytic and xylanolytic properties, it may play an important role in the biogas fermentation process. Closely related variants of MD1^T were also abundant in microbial communities involved in methanogenic fermentation. Based on morphological, phylogenetic and genomic data, the isolated strain can be considered as representing a novel genus in the family Lachnospiraceae, for which the name Variimorphobacter saccharofermentans gen. nov., sp. nov. (type strain MD1^T=DSM 110715^T=JCM 39125^T) is proposed.

Changes in the distribution of membrane lipids during growth of Thermotoga maritima at different temperatures: Indications for the potential mechanism of biosynthesis of ether-bound diabolic acid (membrane-spanning) lipids

Membrane-spanning lipids are present in a wide variety of archaea but they are rarely in bacteria. Nevertheless, the (hyper)thermophilic members of the order Thermotogales harbor tetraester, tetraether, and mixed ether/ester membrane-spanning lipids mostly composed of core lipids derived from diabolic acids, C_30, C₃₂ and C₃₄ dicarboxylic acids with two adjacent mid-chain methyl substituents. Lipid analysis of Thermotoga maritima across growth phases revealed a decrease of the relative abundance of fatty acids together with an increase of diabolic acids with independence of growth temperature. We also identified isomers of C₃₀ and C₃₂ diabolic acids, i.e. dicarboxylic acids with only one methyl group at C-15. Their distribution suggests they are products of the condensation reaction but preferably produced when the length of the acyl chains is not optimal. In comparison with growth at the optimal temperature of 80°C, an increase of glycerol ether-derived lipids was observed at 55°C. Besides, our analysis only detected diabolic acid-containing intact polar lipids with phosphoglycerol (PG) headgroups. Considering these findings, we hypothesize a biosynthetic pathway for the synthesis of membrane-spanning lipids based on PG polar lipid formation, suggesting that the protein catalyzing this process could be a membrane protein. We also identified, by genomic and protein domain analyses, a gene coding for a putative plasmalogen synthase homologue in T. maritima, which is also present in other bacteria producing sn1-alkyl ether lipids but not plasmalogens, suggesting it could be involved in the conversion of the ester to ether bond in the diabolic acids bound in membrane-spanning lipids. Importance Membrane-spanning lipids are unique compounds found in most archaeal membranes, but they are also present in specific bacterial groups like the Thermotogales. The synthesis and physiological role of membrane-spanning lipids in bacteria represent an evolutionary and biochemical open question that points to the differentiation of the membrane lipids composition. Understanding the formation of membrane-spanning lipids is crucial to solving this question and identifying the enzymatic and biochemical mechanism performing this procedure. In the present work, we found changes at the core lipid level, and we propose that the growth phase drives the biosynthesis of these lipids rather than temperature. Our results identified physiological conditions influencing the membrane-spanning lipids biosynthetic process which can further clarify the pathway leading to the biosynthesis of these compounds.

Transcriptome Analysis Reveals Different Responsive Patterns to Nitrogen Deficiency in Two Wheat Near-Isogenic Lines Contrasting for Nitrogen Use Efficiency

Simple Summary

Nitrogen (N) limitation is the key factor for wheat production worldwide. Therefore, the development of genotypes with improved nitrogen use efficiency (NUE) is a prerequisite for sustainable and productive agriculture. Exploring the molecular mechanisms of low N stress tolerance is significant for breeding wheat cultivars with high NUE. To clarify the underlying molecular mechanisms of enhanced resilience to low N in high-NUE wheat, we performed an RNA sequencing (RNA-seq) analysis. In the current research, two wheat near-isogenic lines (NILs) differing dramatically in NUE were used to measure gene expression differences under different N treatments. There was a dramatic difference between two wheat NILs in response to N deficiency at the transcriptional level, and the classification of identified candidate genes may provide new valuable insights into the resilience mechanism of wheat.

Abstract

The development of crop cultivars with high nitrogen use efficiency (NUE) under low-N fertilizer inputs is imperative for sustainable agriculture. However, there has been little research on the molecular mechanisms underlying enhanced resilience to low N in high-NUE plants. The comparison of the transcriptional responses of genotypes contrasting for NUE will facilitate an understanding of the key molecular mechanism of wheat resilience to low-N stress. In the current study, the RNA sequencing (RNA-seq) technique was employed to investigate the genotypic difference in response to N deficiency between two wheat NILs (1Y, high-NUE, and 1W, low-NUE). In our research, high- and low-NUE wheat NILs showed different patterns of gene expression under N-deficient conditions, and these N-responsive genes were classified into two major classes, including “frontloaded genes” and “relatively upregulated genes”. In total, 103 and 45 genes were identified as frontloaded genes in high-NUE and low-NUE wheat, respectively. In summary, our study might provide potential directions for further understanding the molecular mechanism of high-NUE genotypes adapting to low-N stress.

Integrative growth physiology and transcriptome profiling of probiotic Limosilactobacillus reuteri KUB-AC5

Limosilactobacillus reuteri KUB-AC5 has been widely used as probiotic in chicken for Salmonella reduction. However, a preferable carbon source and growth phase is poorly characterized underlying metabolic responses on growth and inhibition effects of L. reuteri KUB-AC5. This study therefore aimed to investigate transcriptome profiling of L. reuteri KUB-AC5 revealing global metabolic responses when alteration of carbon sources and growth phases. Interestingly, L. reuteri KUB-AC5 grown under sucrose culture showed to be the best for fast growth and inhibition effects against Salmonella Enteritidis S003 growth. Towards the transcriptome profiling and reporter proteins/metabolites analysis, the results showed that amino acid transport via ABC systems as well as sucrose metabolism and transport are key metabolic responses at Logarithmic (L)-phase of L. reuteri KUB-AC5 growth. Considering the Stationary (S)-phase, we found the potential reporter proteins/metabolites involved in carbohydrate metabolism e.g., levansucrase and levan. Promisingly, levansucrase and levan were revealed to be candidates in relation to inhibition effects of L. reuteri KUB-AC5. Throughout this study, L. reuteri KUB-AC5 had a metabolic control in acclimatization to sucrose and energy pools through transcriptional co-regulation, which supported the cell growth and inhibition potentials. This study offers a perspective in optimizing fermentation condition through either genetic or physiological approaches for enhancing probiotic L. reuteri KUB-AC5 properties.

Ferroptosis-related gene AKR1C1 predicts the prognosis of non-small cell lung cancer

BACKGROUND

Ferroptosis is a newly discovered mode of cell death distinct from apoptosis and necrosis, and its activation contributes to anticancer therapy in a variety of cancers. However, the prognostic value of ferroptosis-related genes in non-small cell lung cancer (NSCLC) remains to be further investigated.

METHODS

NSCLC transcriptome mRNA-seq data set and corresponding clinical data set were downloaded from the Cancer Genome Atlas (TCGA). Then, bioinformatics approaches were subsequently employed to identify potential prognostic markers. Finally, the effects of candidate markers on NSCLC cell proliferation, migration, and ferroptosis were assessed by CCK8, colony formation, wound-healing assay, and functional assays related to ferroptosis.

RESULTS

A total of 37 common differentially expressed genes were screened based TCGA database. Six overall survival associated genes (ENPP2, ULK1, CP, LURAP1L, HIC1, AKR1C1) were selected to build survival model, of which hub gene AKR1C1 was with high expression and low ferroptosis level in NSCLC tumor. Further research showed that AKR1C1 was related with many pathways involved in the process of ferroptosis and associated with diverse cancer-infiltrating immune cells. Moreover, the results of in vitro experiments indicated that the expression of AKR1C1 was upregulated in NSCLC cell lines, and silencing AKR1C1 can inhibit the proliferation and migration of NSCLC cells and promote the occurrence of ferroptosis.

CONCLUSIONS

Our study revealed the potential role of ferroptosis-related gene AKR1C1 in NSCLC, which can be used for prognostic prediction in NSCLC.

Kinetics of the ancestral carbon metabolism pathways in deep-branching bacteria and archaea

The origin of life is believed to be chemoautotrophic, deriving all biomass components from carbon dioxide, and all energy from inorganic redox couples in the environment. The reductive tricarboxylic acid cycle (rTCA) and the Wood-Ljungdahl pathway (WL) have been recognized as the most ancient carbon fixation pathways. The rTCA of the chemolithotrophic Thermosulfidibacter takaii, which was recently demonstrated to take place via an unexpected reverse reaction of citrate synthase, was reproduced using a kinetic network model, and a competition between reductive and oxidative fluxes on rTCA due to an acetyl coenzyme A (ACOA) influx upon acetate uptake was revealed. Avoiding ACOA direct influx into rTCA from WL is, therefore, raised as a kinetically necessary condition to maintain a complete rTCA. This hypothesis was confirmed for deep-branching bacteria and archaea, and explains the kinetic factors governing elementary processes in carbon metabolism evolution from the last universal common ancestor.

Genotyping Analysis by RAD-Seq Reads Is Useful to Assess the Genetic Identity and Relationships of Breeding Lines in Lavender Species Aimed at Managing Plant Variety Protection

Lavender species are widely distributed in their wild forms around the Mediterranean Basin and they are also cultivated worldwide as improved and registered clonal varieties. The economic interest of the species belonging to the Lavandula genus is determined by their use as ornamental plants and important source of essential oils that are destinated to the production of cosmetics, pharmaceuticals and foodstuffs. Because of the increasing number of cases of illegal commercialization of selected varieties, the protection of plant breeders’ rights has become of main relevance for the recognition of breeding companies’ royalties. With this aim, genomic tools based on molecular markers have been demonstrated to be very reliable and transferable among laboratories, and also much more informative than morphological descriptors. With the rising of the next-generation sequencing (NGS) technologies, several genotyping-by-sequencing approaches are now available. This study deals with a deep characterization of 15 varietal clones, belonging to two distinct Lavandula species, by means of restriction-site associated DNA sequencing (RAD-Seq). We demonstrated that this technology screens single nucleotide variants that enable to assess the genetic identity of individual accessions, to reconstruct genetic relationships among related breeding lines, to group them into genetically distinguishable main subclusters, and to assign their molecular lineages to distinct ancestors. Moreover, a number of polymorphic sites were identified within genes putatively involved in biosynthetic pathways related to both tissue pigmentation and terpene production, useful for breeding and/or protecting newly registered varieties. Overall, the results highlighted the presence of pure ancestries and interspecific hybrids for the analyzed Lavandula species, and demonstrated that RAD-Seq analysis is very informative and highly reliable for characterizing Lavandula clones and managing plant variety protection.

A Systems Approach to Interrogate Gene Expression Patterns in African American Men Presenting with Clinically Localized Prostate Cancer

Simple Summary

Men of African origin have a 2–3 times greater chance of developing prostate cancer than those of European origin, and of patients that are diagnosed with the disease, men of African descent are 2 times more likely to die compared to white men. Men of African origin are still greatly underrepresented in genetic studies and clinical trials. This, unfortunately, means that new discoveries in cancer treatment are missing key information on the group with a greater chance of mortality. The objective of this study was to increase our knowledge of prostate cancer in men undergoing a prostate biopsy. We carried out RNA sequencing of biopsy specimens and examined racial differences in prostate gene expression. A gene expression signature was uncovered which separated the men based on their race. Furthermore, within men of African descent this signature separated men with the most severe clinical characteristics.

Abstract

An emerging theory about racial differences in cancer risk and outcomes is that psychological and social stressors influence cellular stress responses; however, limited empirical data are available on racial differences in cellular stress responses among men who are at risk for adverse prostate cancer outcomes. In this study, we undertook a systems approach to examine molecular profiles and cellular stress responses in an important segment of African American (AA) and European American (EA) men: men undergoing prostate biopsy. We assessed the prostate transcriptome with a single biopsy core via high throughput RNA sequencing (RNA-Seq). Transcriptomic analyses uncovered impacted biological pathways including PI3K-Akt signaling pathway, Neuroactive ligand-receptor interaction pathway, and ECM-receptor interaction. Additionally, 187 genes mapping to the Gene Ontology (GO) terms RNA binding, structural constituent of ribosome, SRP-dependent co-translational protein targeting to membrane and the biological pathways, translation, L13a-mediated translational silencing of Ceruloplasmin expression were differentially expressed (DE) between EA and AA. This signature allowed separation of AA and EA patients, and AA patients with the most severe clinical characteristics. AA patients with elevated expression levels of this genomic signature presented with higher Gleason scores, a greater number of positive core biopsies, elevated dehydroepiandrosterone sulfate levels and serum vitamin D deficiency. Protein-protein interaction (PPI) network analysis revealed a high degree of connectivity between these 187 proteins.

The Divergent Roles of the Rice bcl-2 Associated Athanogene (BAG) Genes in Plant Development and Environmental Responses

Bcl-2-associated athanogene (BAG), a group of proteins evolutionarily conserved and functioned as co-chaperones in plants and animals, is involved in various cell activities and diverse physiological processes. However, the biological functions of this gene family in rice are largely unknown. In this study, we identified a total of six BAG members in rice. These genes were classified into two groups, OsBAG1, -2, -3, and -4 are in group I with a conserved ubiquitin-like structure and OsBAG5 and -6 are in group Ⅱ with a calmodulin-binding domain, in addition to a common BAG domain. The BAG genes exhibited diverse expression patterns, with OsBAG4 showing the highest expression level, followed by OsBAG1 and OsBAG3, and OsBAG6 preferentially expressed in the panicle, endosperm, and calli. The co-expression analysis and the hierarchical cluster analysis indicated that the OsBAG1 and OsBAG3 were co-expressed with primary cell wall-biosynthesizing genes, OsBAG4 was co-expressed with phytohormone and transcriptional factors, and OsBAG6 was co-expressed with disease and shock-associated genes. β-glucuronidase (GUS) staining further indicated that OsBAG3 is mainly involved in primary young tissues under both primary and secondary growth. In addition, the expression of the BAG genes under brown planthopper (BPH) feeding, N, P, and K deficiency, heat, drought and plant hormones treatments was investigated. Our results clearly showed that OsBAGs are multifunctional molecules as inferred by their protein structures, subcellular localizations, and expression profiles. BAGs in group I are mainly involved in plant development, whereas BAGs in group II are reactive in gene regulations and stress responses. Our results provide a solid basis for the further elucidation of the biological functions of plant BAG genes.

Construction of competing endogenous RNA interaction network as prognostic markers in metastatic melanoma

Malignant melanoma (MM) is a malignant tumor originating from melanocytes, with high aggressiveness, high metastasis and extremely poor prognosis. MM accounts for 4% of skin cancers and 80% of mortality, and the median survival of patients with metastatic melanoma is only about 6 months, with a five-year survival rate of less than 10%. In recent years, the incidence of melanoma has gradually increased and has become one of the serious diseases that endanger human health. Competitive endogenous RNA (ceRNA) is the main model of the mechanism by which long chain non-coding RNAs (lncRNAs) play a regulatory role in the disease. LncRNAs can act as a "sponge", competitively attracting small RNAs (micoRNAs; miRNAs), thus interfering with miRNA function, and affect the expression of target gene messenger RNAs (mRNAs), ultimately promoting tumorigenesis and progression. Bioinformatics analysis can identify potentially prognostic and therapeutically relevant differentially expressed genes in MM, finding lncRNAs, miRNAs and mRNAs that are interconnected through the ceRNA network, providing further insight into gene regulation and prognosis of metastatic melanoma. Weighted co-expression networks were used to identify lncRNA and mRNA modules associated with the metastatic phenotype, as well as the co-expression genes contained in the modules. A total of 17 lncRNAs, six miRNAs, and 11 mRNAs were used to construct a ceRNA interaction network that plays a regulatory role in metastatic melanoma patients. The prognostic risk model was used as a sorter to classify the survival prognosis of melanoma patients. Four groups of ceRNA interaction triplets were finally obtained, which miR-3662 might has potential implication for the treatment of metaststic melanoma patients, and futher experiments confirmed the regulating relationship and phenotype of this assumption. This study provides new targets to regulate metastatic process, predict metastatic potential and indicates that the miR-3662 can be used in the treatment of melanoma.

Tanshinone IIA affects the malignant growth of Cholangiocarcinoma cells by inhibiting the PI3K-Akt-mTOR pathway

In the present study, we aimed to find the target of Tanshinone IIA (Tan-IIA) in Cholangiocarcinoma by network pharmacology-based prediction and investigate the possible mechanism through experimental verification. In this study, we combined Tan-IIA-specific and Cholangiocarcinoma-specific targets with protein–protein interactions (PPI) to construct a Tan-IIA targets-Cholangiocarcinoma network, and network pharmacology approach was applied to identify potential targets and mechanisms of Tan-IIA in the treatment of Cholangiocarcinoma. The anti-cancer effects of Tan-IIA were investigated by using subcutaneous tumorigenic model in nude mice and in the human Cholangiocarcinoma cell lines in vitro. Our results showed that Tan-IIA treatment considerably suppressed the proliferation and migration of Cholangiocarcinoma cells while inducing apoptosis of Cholangiocarcinoma cells. Western blot results demonstrated that the expression of PI3K, p-Akt, p-mTOR, and mTOR were inhibited by Tan-IIA. Meanwhile, After treatment with Tan-IIA, the level of Bcl2 was downregulated and cleaved caspase-3 expression increased. Further studies revealed that the anticancer effects of Tan-IIA were severely mitigated by pretreatment with a PI3K agonist. Our research provides a new anticancer strategy and strengthens support for the use of Tan-IIA as an anticancer drug for the treatment of CCA.

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning

One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand-target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.

Construction of a 5 immune-related lncRNA-based prognostic model of NSCLC via bioinformatics

Participate in tumorigenic, oncogenic, and tumor suppressive pathways through gene expression regulation. We aimed to build an immune-related long noncoding RNA (lncRNA) prognostic model to enhance nonsmall cell lung cancer (NSCLC) prognostic prediction.The original data were collected from the cancer genome atlas database. Perl and R software were used for statistical analysis. The effects of lncRNAs expression on prognosis were analyzed by Gene Expression Profiling Interactive Analysis. Silico functional analysis were performed by DAVID Bioinformatics Resources.The median risk score as a dividing value separated patients into high- and low-risk groups. These 2 groups had different 5-year survival rates, median survival times, and immune statuses. The 5-lncRNA signature was validated as an independent prognostic factor with high accuracy (area under the receiver operating characteristic = 0.722). Silico functional analysis connected the lncRNAs with immune-related biological processes and pathways in carcinogenesis.The novel immune-related lncRNA prognostic model had significant clinical implication for enhancing lung adenocarcinoma outcome prediction and guiding the choice of treatment.

Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin

Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. To this purpose, we propose a "digital twin" of patients modeling the human body as a whole and providing a panoramic view over individuals' conditions. Methods: We propose a general framework that composes advanced artificial intelligence (AI) approaches and integrates mathematical modeling in order to provide a panoramic view over current and future pathophysiological conditions. Our modular architecture is based on a graph neural network (GNN) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GAN) providing a proof of concept of transcriptomic integrability. Results: We tested our digital twin model on two simulated clinical case studies combining information at organ, tissue, and cellular level. We provided a panoramic overview over current and future patient's conditions by monitoring and forecasting clinically relevant endpoints representing the evolution of patient's vital parameters using the GNN model. We showed how to use the GAN to generate multi-tissue expression data for blood and lung to find associations between cytokines conditioned on the expression of genes in the renin-angiotensin pathway. Our approach was to detect inflammatory cytokines, which are known to have effects on blood pressure and have previously been associated with SARS-CoV-2 infection (e.g., CXCR6, XCL1, and others). Significance: The graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modeling with AI. We believe that this work represents a step forward toward next-generation devices for precision and predictive medicine.

Genetically variant human pluripotent stem cells selectively eliminate wild-type counterparts through YAP-mediated cell competition

The appearance of genetic changes in human pluripotent stem cells (hPSCs) presents a concern for their use in research and regenerative medicine. Variant hPSCs that harbor recurrent culture-acquired aneuploidies display growth advantages over wild-type diploid cells, but the mechanisms that yield a drift from predominantly wild-type to variant cell populations remain poorly understood. Here, we show that the dominance of variant clones in mosaic cultures is enhanced through competitive interactions that result in the elimination of wild-type cells. This elimination occurs through corralling and mechanical compression by faster-growing variants, causing a redistribution of F-actin and sequestration of yes-associated protein (YAP) in the cytoplasm that induces apoptosis in wild-type cells. YAP overexpression or promotion of YAP nuclear localization in wild-type cells alleviates their "loser" phenotype. Our results demonstrate that hPSC fate is coupled to mechanical cues imposed by neighboring cells and reveal that hijacking this mechanism allows variants to achieve clonal dominance in cultures.

Phosphoproteomics analysis of diabetic cardiomyopathy in aging-accelerated mice and effects of D-pinitol

PURPOSE

The molecular mechanisms of diabetic cardiomyopathy (DCM) development and D-pinitol (DP) in its treatment remain unclear. The present study is to explore the underlying mechanism of DCM in an elderly diabetic mouse model and to seek the protective targets of DP by phosphoproteomics.

EXPERIMENTAL DESIGN

We used streptozotocin to induce diabetes in SAMP8 and DP (150 mg/kg/day) intragastrically administrated to diabetic mice for 8 weeks. The heart tissues were harvested for label-free phosphoproteomic analysis from diabetic mice. Some differentially regulated phosphorylation sites were confirmed by parallel reaction monitoring.

RESULTS

Our results showed that 612 phosphorylation sites on 454 proteins had their phosphorylation levels significantly changed in the heart of untreated diabetic mice (DM). Of these phosphorylation sites, 216 phosphorylation sites on 182 proteins were normalized after DP treatment. We analyzed the functional signaling pathways in the heart of DP treated diabetic mice (DMT), including glucagon signaling pathway, insulin signaling pathway, mitophagy, apoptosis, and longevity regulating pathway. Two consensus motifs identified were targeted by Src and epidermal growth factor receptor between DMT and DM groups.

CONCLUSIONS AND CLINICAL RELEVANCE

Our study might help to better understand the mechanism of DCM, provide novel targets for estimating the protective effects of DP.

The Tudor Domain-Containing Protein BbTdp1 Contributes to Fungal Cell Development, the Cell Cycle, Virulence, and Transcriptional Regulation in the Insect Pathogenic Fungus Beauveria bassiana

Beauveria bassiana is an insect pathogenic fungus that serves as a model system for exploring the mechanisms of fungal development and host-pathogen interactions. Clinical and experimental studies have indicated that SND1 is closely correlated with the progression and invasiveness of common cancers as a potential oncogene, but this gene has rarely been studied in fungi. Here, we characterized the contributions of an SND1 ortholog (Tdp1) by constructing a BbTdp1 deletion strain and a complemented strain of B. bassiana. Compared with the wild-type (WT) strain, the ΔBbTdp1 mutant lost conidiation capacity (∼87.7%) and blastospore (∼96.3%) yields, increased sensitivity to chemical stress (4.4 to 54.3%) and heat shock (∼44.2%), and decreased virulence following topical application (∼24.7%) and hemocoel injection (∼40.0%). Flow cytometry readings showed smaller sizes of both conidia and blastospores for ΔBbTdp1 mutants. Transcriptomic data revealed 4,094 differentially expressed genes (|log2 ratio| > 2 and a q value of <0.05) between ΔBbTdp1 mutants and the WT strain, which accounted for 41.6% of the total genes, indicating that extreme fluctuation in the global gene expression pattern had occurred. Moreover, deletion of BbTdp1 led to an abnormal cell cycle with a longer S phase and shorter G2/M and G0/G1 phases of blastospores, and enzyme-linked immunosorbent assay confirmed that the level of phosphorylated cyclin-dependent kinase 1 (Cdk1) in the ΔBbTdp1 strain was ∼31.5% lower than in the WT strain. In summary, our study is the first to report that BbTdp1 plays a vital role in regulating conidia and blastospore yields, fungal morphological changes, and pathogenicity in entomopathogenic fungi. IMPORTANCE In this study, we used Beauveria bassiana as a biological model to report the role of BbTdp1 in entomopathogenic fungi. Our findings indicated that BbTdp1 contributed significantly to cell development, the cell cycle, and virulence in B. bassiana. In addition, deletion of BbTdp1 led to drastic fluctuations in the transcriptional profile. BbTdp1 can be developed as a novel target for B. bassiana development and pathogenicity, which also provides a framework for the study of Tdp1 in other fungi.

Holistic evaluation of biodegradation pathway prediction: assessing multi-step reactions and intermediate products

The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental sciences, drug discovery, and (eco)toxicology. Several systems predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their performance is commonly evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are caused by specific properties of biotransformation experiments. That is, missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rule-based prediction systems evaluate the performance only based on the defined set of transformation rules. Therefore, the performance of these models cannot be directly compared. In this paper, we introduce a new evaluation framework that extends the evaluation of biotransformation prediction from single transformations to whole pathways, taking into account multiple generations of metabolites. We introduce a procedure to address transient intermediates and propose a weighted scoring system that acknowledges the uncertainty of higher-generation metabolites. We implemented this framework in enviPath and demonstrate its strict performance metrics on predictions of in vitro biotransformation and degradation of xenobiotics in soil. Our approach is model-agnostic and can be transferred to other prediction systems. It is also capable of revealing knowledge gaps in terms of incompletely defined sets of transformation rules.

Time-Resolved Proteome Analysis of Listeria monocytogenes during Infection Reveals the Role of the AAA+ Chaperone ClpC for Host Cell Adaptation

The cellular proteome comprises all proteins expressed at a given time and defines an organism's phenotype under specific growth conditions. The proteome is shaped and remodeled by both protein synthesis and protein degradation. Here, we developed a new method which combines metabolic and chemical isobaric peptide labeling to simultaneously determine the time-resolved protein decay and de novo synthesis in an intracellular human pathogen. We showcase this method by investigating the Listeria monocytogenes proteome in the presence and absence of the AAA+ chaperone protein ClpC. ClpC associates with the peptidase ClpP to form an ATP-dependent protease complex and has been shown to play a role in virulence development in L. monocytogenes. However, the mechanism by which ClpC is involved in the survival and proliferation of intracellular L. monocytogenes remains elusive. Employing this new method, we observed extensive proteome remodeling in L. monocytogenes upon interaction with the host, supporting the hypothesis that ClpC-dependent protein degradation is required to initiate bacterial adaptation mechanisms. We identified more than 100 putative ClpC target proteins through their stabilization in a clpC deletion strain. Beyond the identification of direct targets, we also observed indirect effects of the clpC deletion on the protein abundance in diverse cellular and metabolic pathways, such as iron acquisition and flagellar assembly. Overall, our data highlight the crucial role of ClpC for L. monocytogenes adaptation to the host environment through proteome remodeling. IMPORTANCE Survival and proliferation of pathogenic bacteria inside the host depend on their ability to adapt to the changing environment. Profiling the underlying changes on the bacterial proteome level during the infection process is important to gain a better understanding of the pathogenesis and the host-dependent adaptation processes. The cellular protein abundance is governed by the interplay between protein synthesis and decay. The direct readout of these events during infection can be accomplished using pulsed stable-isotope labeling by amino acids in cell culture (SILAC). Combining this approach with tandem-mass-tag (TMT) labeling enabled multiplexed and time-resolved bacterial proteome quantification during infection. Here, we applied this integrated approach to investigate protein turnover during the temporal progression of adaptation of the human pathogen L. monocytogenes to its host on a system-wide scale. Our experimental approach can easily be transferred to probe the proteome remodeling in other bacteria under a variety of perturbations.