Argonaute and Dicer are essential for communication between Trichoderma atroviride and fungal hosts during mycoparasitism

Trichoderma species are known for their mycoparasitic activity against phytopathogenic fungi that cause significant economic losses in agriculture. During mycoparasitism, Trichoderma spp. recognize molecules produced by the host fungus and release secondary metabolites and hydrolytic enzymes to kill and degrade the host's cell wall. Here, we explored the participation of the Trichoderma atroviride RNAi machinery in the interaction with six phytopathogenic fungi of economic importance. We determined that both Argonaute-3 and Dicer-2 play an essential role during mycoparasitism. Using an RNA-Seq approach, we identified that perception, detox, and cell wall degradation depend on the T. atroviride-RNAi when interacting with Alternaria alternata, Rhizoctonia solani AG2, and R. solani AG5. Furthermore, we constructed a gene co-expression network that provides evidence of two gene modules regulated by RNAi, which play crucial roles in essential processes during mycoparasitism. In addition, based on small RNA-seq, we conclude that siRNAs regulate amino acid and carbon metabolism and communication during the Trichoderma-host interaction. Interestingly, our data suggest that siRNAs might regulate allorecognition (het) and transport genes in a cross-species manner. Thus, these results reveal a fine-tuned regulation in T. atroviride dependent on siRNAs that is essential during the biocontrol of phytopathogenic fungi, showing a greater complexity of this process than previously established.IMPORTANCEThere is an increasing need for plant disease control without chemical pesticides to avoid environmental pollution and resistance, and the health risks associated with the application of pesticides are increasing. Employing Trichoderma species in agriculture to control fungal diseases is an alternative plant protection strategy that overcomes these issues without utilizing chemical fungicides. Therefore, understanding the biocontrol mechanisms used by Trichoderma species to antagonize other fungi is critical. Although there has been extensive research about the mechanisms involved in the mycoparasitic capability of Trichoderma species, there are still unsolved questions related to how Trichoderma regulates recognition, attack, and defense mechanisms during interaction with a fungal host. In this work, we report that the Argonaute and Dicer components of the RNAi machinery and the small RNAs they process are essential for gene regulation during mycoparasitism by Trichoderma atroviride.

Quantifying Dynamic Phenotypic Heterogeneity in Resistant Escherichia coli under Translation-Inhibiting Antibiotics

Understanding the phenotypic heterogeneity of antibiotic-resistant bacteria following treatment and the transitions between different phenotypes is crucial for developing effective infection control strategies. The study expands upon previous work by explicating chloramphenicol-induced phenotypic heterogeneities in growth rate, gene expression, and morphology of resistant Escherichia coli using time-lapse microscopy. Correlating the bacterial growth rate and cspC expression, four interchangeable phenotypic subpopulations across varying antibiotic concentrations are identified, surpassing the previously described growth rate bistability. Notably, bacterial cells exhibiting either fast or slow growth rates can concurrently harbor subpopulations characterized by high and low gene expression levels, respectively. To elucidate the mechanisms behind this enhanced heterogeneity, a concise gene expression network model is proposed and the biological significance of the four phenotypes is further explored. Additionally, by employing Hidden Markov Model fitting and integrating the non-equilibrium landscape and flux theory, the real-time data encompassing diverse bacterial traits are analyzed. This approach reveals dynamic changes and switching kinetics in different cell fates, facilitating the quantification of observable behaviors and the non-equilibrium dynamics and thermodynamics at play. The results highlight the multi-dimensional heterogeneous behaviors of antibiotic-resistant bacteria under antibiotic stress, providing new insights into the compromised antibiotic efficacy, microbial response, and associated evolution processes.

Identification of novel potential drugs and miRNAs biomarkers in lung cancer based on gene co-expression network analysis

Non-small cell lung cancer (NSCLC) is an important cause of cancer-associated deaths worldwide. Therefore, the exact molecular mechanisms of NSCLC are unidentified. The present investigation aims to identify the miRNAs with predictive value in NSCLC. The two datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEmiRNA) and mRNAs (DEmRNA) were selected from the normalized data. Next, miRNA-mRNA interactions were determined. Then, co-expression network analysis was completed using the WGCNA package in R software. The co-expression network between DEmiRNAs and DEmRNAs was calculated to prioritize the miRNAs. Next, the enrichment analysis was performed for DEmiRNA and DEmRNA. Finally, the drug-gene interaction network was constructed by importing the gene list to dgidb database. A total of 3,033 differentially expressed genes and 58 DE miRNA were recognized from two datasets. The co-expression network analysis was utilized to build a gene co-expression network. Next, four modules were selected based on the Zsummary score. In the next step, a bipartite miRNA-gene network was constructed and hub miRNAs (let-7a-2-3p, let-7d-5p, let-7b-5p, let-7a-5p, and let-7b-3p) were selected. Finally, a drug-gene network was constructed while SUNITINIB, MEDROXYPROGESTERONE ACETATE, DOFETILIDE, HALOPERIDOL, and CALCITRIOL drugs were recognized as a beneficial drug in NSCLC. The hub miRNAs and repurposed drugs may act a vital role in NSCLC progression and treatment, respectively; however, these results must validate in further clinical and experimental assessments.

Five OsS40 Family Members Are Identified as Senescence-Related Genes in Rice by Reverse Genetics Approach

A total of 16 OsS40 genes of Oryza sativa were identified in our previous work, but their functions remain unclear. In this study, 13 OsS40 members were knocked out using the CRISPR/cas9 gene-editing technology. After screening phenotype characterization of CRISPR/Cas9 mutants compared to WT, five oss40s mutants exhibited a stay-green phenotype at 30 days after heading. Moreover, increased grain size and grain weight occurred in the oss40-1, oss40-12, and oss40-14 lines, while declined grain weight appeared in the oss40-7 and oss40-13 mutants. The transcript levels of several senescence-associated genes (SAGs), chlorophyll degradation-related genes (CDGs), as well as WRKY members were differentially decreased in the five stay-green oss40s mutants compared to WT. Five oss40 mutants also exhibited a stay-green phenotype when the detached leaves were incubated under darkness for 4 days. OsSWEET4 and OsSWEET1b were significantly upregulated, while OsSWEET1a and OsSWEET13 were significantly downregulated in both oss40-7 and oss40-14 compared to WT. Furthermore, these five OsS40 displayed strong transcriptional activation activity and were located in the nucleus. Most of the OsS40 genes were downregulated in the oss40-1, oss40-7, and oss40-12 mutants, but upregulated in the oss40-13 and oss40-14 mutants, indicating coordinated regulation among OsS40 members. These results suggest that OsS40-1, OsS40-7, OsS40-12, OsS40-13, and OsS40-14 are senescence-associated genes, involved in the senescence and carbon allocation network by modulating other OsS40 members, SWEET member genes, and senescence-related gene expression.

Network analysis, sequence and structure dynamics of key proteins of coronavirus and human host, and molecular docking of selected phytochemicals of nine medicinal plants

The novel coronavirus of 2019 (nCoV-19) has become a pandemic, affecting over 205 nations with over 7,410,000 confirmed cases which has resulted to over 418,000 deaths worldwide. This study aimed to identify potential therapeutic compounds and phytochemicals of medicinal plants that have potential to modulate the expression network of genes that are involve in SARS-CoV-2 pathology in human host and to understand the dynamics key proteins involved in the virus-host interactions. The method used include gene network analysis, molecular docking, and sequence and structure dynamics simulations. The results identified DNA-dependent protein kinase (DNA-PK) and Protein kinase CK2 as key players in SARS-CoV-2 lifecycle. Among the predicted drugs compounds, clemizole, monorden, spironolactone and tanespimycin showed high binding energies; among the studied repurposing compounds, remdesivir, simeprevir and valinomycin showed high binding energies; among the predicted acidic compounds, acetylursolic acid and hardwickiic acid gave high binding energies; while among the studied anthraquinones and glycosides compounds, ellagitannin and friedelanone showed high binding energies against 3-Chymotrypsin-like protease (3CL^pro), Papain-like protease (PL^pro), helicase (nsp13), RNA-dependent RNA polymerase (nsp12), 2'-O-ribose methyltransferase (nsp16) of SARS-CoV-2 and DNA-PK and CK2alpha in human. The order of affinity for CoV proteins is 5Y3E > 6NUS > 6JYT > 2XYR > 3VB6. Finally, medicinal plants with phytochemicals such as caffeine, ellagic acid, quercetin and their derivatives could possibly remediate COVID-19.Communicated by Ramaswamy H. Sarma.

Transcriptional Insight Into Brassica napus Resistance Genes LepR3 and Rlm2-Mediated Defense Response Against the Leptosphaeria maculans Infection

The phytopathogenic fungus Leptosphaeria maculans causes the blackleg disease on Brassica napus, resulting in severe loss of rapeseed production. Breeding of resistant cultivars containing race-specific resistance genes is provably effective to combat this disease. While two allelic resistance genes LepR3 and Rlm2 recognizing L. maculans avirulence genes AvrLm1 and AvrLm2 at plant apoplastic space have been cloned in B. napus, the downstream gene expression network underlying the resistance remains elusive. In this study, transgenic lines expressing LepR3 and Rlm2 were created in the susceptible "Westar" cultivar and inoculated with L. maculans isolates containing different sets of AvrLm1 and AvrLm2 for comparative transcriptomic analysis. Through grouping the RNA-seq data based on different levels of defense response, we find LepR3 and Rlm2 orchestrate a hierarchically regulated gene expression network, consisting of induced ABA acting independently of the disease reaction, activation of signal transduction pathways with gradually increasing intensity from compatible to incompatible interaction, and specifically induced enzymatic and chemical actions contributing to hypersensitive response with recognition of AvrLm1 and AvrLm2. This study provides an unconventional investigation into LepR3 and Rlm2-mediated plant defense machinery and adds novel insight into the interaction between surface-localized receptor-like proteins (RLPs) and apoplastic fungal pathogens.

Divergent Expression Patterns in Two Vernicia Species Revealed the Potential Role of the Hub Gene VmAP2/ERF036 in Resistance to Fusarium oxysporum in Vernicia montana

Tung oil tree (Vernicia fordii) is a promising industrial oil crop; however, this tree is highly susceptible to Fusarium wilt disease. Conversely, Vernicia montana is resistant to the pathogen. The APETALA2/ethylene-responsive element binding factor (AP2/ERF) transcription factor superfamily has been reported to play a significant role in resistance to Fusarium oxysporum. In this study, comprehensive analysis identified 75 and 81 putative Vf/VmAP2/ERF transcription factor-encoding genes in V. fordii and V. montana, respectively, which were divided into AP2, ERF, related to ABI3 and VP1 (RAV) and Soloist families. After F. oxysporum infection, a majority of AP2/ERF superfamily genes showed strong patterns of repression in both V. fordii and V. montana. We then identified 53 pairs of one-to-one orthologs in V. fordii and V. montana, with most pairs of orthologous genes exhibiting similar expression in response to the pathogen. Further investigation of Vf/VmAP2/ERF gene expression in plant tissues indicated that the pairs of genes with different expression patterns in response to F. oxysporum tended to exhibit different tissue profiles in the two species. In addition, VmAP2/ERF036, showing the strongest interactions with 666 genes, was identified as a core hub gene mediating resistance. Moreover, qRT-PCR results indicated VmAP2/ERF036 showed repressed expression while its orthologous gene VfAP2/ERF036 had the opposite expression pattern during pathogen infection. Overall, comparative analysis of the Vf/VmAP2/ERF superfamily and indication of a potential hub resistance gene in resistant and susceptible Vernicia species provides valuable information for understanding the molecular basis and selection of essential functional genes for V. fordii genetic engineering to control Fusarium wilt disease.

OryzaExpress: an integrated database of gene expression networks and omics annotations in rice

Similarity of gene expression profiles provides important clues for understanding the biological functions of genes, biological processes and metabolic pathways related to genes. A gene expression network (GEN) is an ideal choice to grasp such expression profile similarities among genes simultaneously. For GEN construction, the Pearson correlation coefficient (PCC) has been widely used as an index to evaluate the similarities of expression profiles for gene pairs. However, calculation of PCCs for all gene pairs requires large amounts of both time and computer resources. Based on correspondence analysis, we developed a new method for GEN construction, which takes minimal time even for large-scale expression data with general computational circumstances. Moreover, our method requires no prior parameters to remove sample redundancies in the data set. Using the new method, we constructed rice GENs from large-scale microarray data stored in a public database. We then collected and integrated various principal rice omics annotations in public and distinct databases. The integrated information contains annotations of genome, transcriptome and metabolic pathways. We thus developed the integrated database OryzaExpress for browsing GENs with an interactive and graphical viewer and principal omics annotations (http://riceball.lab.nig.ac.jp/oryzaexpress/). With integration of Arabidopsis GEN data from ATTED-II, OryzaExpress also allows us to compare GENs between rice and Arabidopsis. Thus, OryzaExpress is a comprehensive rice database that exploits powerful omics approaches from all perspectives in plant science and leads to systems biology.