Verticillium dahliae causes destructive vascular wilt diseases on more than 200 plant species, including economically important crops and ornamental trees worldwide. The melanized microsclerotia enable the fungus to survive for years in soil and are crucial for its disease cycle. Previously, we found that the VdPbs2-VdHog1 (V. dahliae Pbs2-V. dahliae Hog1) module plays key roles in microsclerotial formation, stress responses, and virulence in V. dahliae. In this study, two mitogen-activated protein kinase kinase kinases (MAPKKKs) homologous to Ssk2p and Ste11p, which activate the Pbs2p-Hog1p module by phosphorylation in budding yeast, were identified in the genome of V. dahliae. Both ΔVdSsk2 (V. dahliae Ssk2) and ΔVdSte11 strains showed severe defects in microsclerotial formation and melanin biosynthesis, but the relative importance of these two genes in microsclerotial development was different. Deletion of VdSsk2, but not VdSte11, affected responses to osmotic stress, fungicidal response, and cell wall stressors. The ΔVdSsk2 strain exhibited a significant reduction in virulence, while the ΔVdSte11 strain was nonpathogenic due to failure to penetrate and form hyphopodia. Phosphorylation assays demonstrated that VdSsk2, but not VdSte11, can phosphorylate VdHog1 in V. dahliae. Moreover, VdCrz1, encoding a calcineurin-responsive zinc finger transcription factor and a key regulator of calcium signaling in fungi, was misregulated in the ΔVdSsk2, ΔVdPbs2, and ΔVdHog1 mutants. IMPORTANCE These data provide insights into the distinctive functions of VdSsk2 and VdSte11 in pathogenicity, stress adaptation, and microsclerotial formation in V. dahliae.

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Here, we present a multi-modal deep generative model, the single-cell Multi-View Profiler (scMVP), which is designed for handling sequencing data that simultaneously measure gene expression and chromatin accessibility in the same cell, including SNARE-seq, sci-CAR, Paired-seq, SHARE-seq, and Multiome from 10X Genomics. scMVP generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification. scMVP can help mitigate data sparsity issues with imputation and accurately identify cell groups for different joint profiling techniques with common latent embedding, and we demonstrate its advantages on several realistic datasets.

Synthetic lethality is emerging as an important cancer therapeutic paradigm, while the comprehensive selective treatment opportunities for various tumors have not yet been explored. We develop the Synthetic Lethality Knowledge Graph (SLKG), presenting the tumor therapy landscape of synthetic lethality (SL) and synthetic dosage lethality (SDL). SLKG integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of SL and SDL pairs. The overall therapy landscape is prioritized to identify the best repurposable drug candidates and drug combinations with literature supports, in vitro pharmacologic evidence or clinical trial records. Finally, cladribine, an FDA-approved multiple sclerosis treatment drug, is selected and identified as a repurposable drug for treating melanoma with CDKN2A mutation by in vitro validation, serving as a demonstrating SLKG utility example for novel tumor therapy discovery. Collectively, SLKG forms the computational basis to uncover cancer-specific susceptibilities and therapy strategies based on the principle of synthetic lethality.