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1. Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets

   https://doi.org/10.3390/jpm11030193  

   Wooten, David J.; Gebru, Melat; Wang, Hong-Gang; Albert, Réka (March 2021, Journal of Personalized Medicine)

   null (Ed.)

   FLT3-mutant acute myeloid leukemia (AML) is an aggressive form of leukemia with poor prognosis. Treatment with FLT3 inhibitors frequently produces a clinical response, but the disease nevertheless often recurs. Recent studies have revealed system-wide gene expression changes in FLT3-mutant AML cell lines in response to drug treatment. Here we sought a systems-level understanding of how these cells mediate these drug-induced changes. Using RNAseq data from AML cells with an internal tandem duplication FLT3 mutation (FLT3-ITD) under six drug treatment conditions including quizartinib and dexamethasone, we identified seven distinct gene programs representing diverse biological processes involved in AML drug-induced changes. Based on the literature knowledge about genes from these modules, along with public gene regulatory network databases, we constructed a network of FLT3-ITD AML. Applying the BooleaBayes algorithm to this network and the RNAseq data, we created a probabilistic, data-driven dynamical model of acquired resistance to these drugs. Analysis of this model reveals several interventions that may disrupt targeted parts of the system-wide drug response. We anticipate co-targeting these points may result in synergistic treatments that can overcome resistance and prevent eventual recurrence. 

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2. Targeting STAT3 anti-apoptosis pathways with organic and hybrid organic–inorganic inhibitors

   https://doi.org/10.1039/C9OB02682G  

   Minus, Matthew B.; Wang, Haopei; Munoz, Jaime O.; Stevens, Alexandra M.; Mangubat-Medina, Alicia E.; Krueger, Michael J.; Liu, Wei; Kasembeli, Moses M.; Cooper, Julian C.; Kolosov, Mikhail I.; et al (May 2020, Organic & Biomolecular Chemistry)

   Recurrence and drug resistance are major challenges in the treatment of acute myeloid leukemia (AML) that spur efforts to identify new clinical targets and active agents. STAT3 has emerged as a potential target in resistant AML, but inhibiting STAT3 function has proven challenging. This paper describes synthetic studies and biological assays for a naphthalene sulfonamide inhibitor class of molecules that inhibit G-CSF-induced STAT3 phosphorylation in cellulo and induce apoptosis in AML cells. We describe two different approaches to inhibitor design: first, variation of substituents on the naphthalene sulfonamide core allows improvements in anti-STAT activity and creates a more thorough understanding of anti-STAT SAR. Second, a novel approach involving hybrid sulfonamide–rhodium( ii ) conjugates tests our ability to use cooperative organic–inorganic binding for drug development, and to use SAR studies to inform metal conjugate design. Both approaches have produced compounds with improved binding potency. In vivo and in cellulo experiments further demonstrate that these approaches can also lead to improved activity in living cells, and that compound 3aa slows disease progression in a xenograft model of AML. 

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3. Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions

   https://doi.org/10.3390/cancers14194763  

   Zhang, Ting-He; Hasib, Md Musaddaqul; Chiu, Yu-Chiao; Han, Zhi-Feng; Jin, Yu-Fang; Flores, Mario; Chen, Yidong; Huang, Yufei (October 2022, Cancers)

   Deep learning has been applied in precision oncology to address a variety of gene expression-based phenotype predictions. However, gene expression data’s unique characteristics challenge the computer vision-inspired design of popular Deep Learning (DL) models such as Convolutional Neural Network (CNN) and ask for the need to develop interpretable DL models tailored for transcriptomics study. To address the current challenges in developing an interpretable DL model for modeling gene expression data, we propose a novel interpretable deep learning architecture called T-GEM, or Transformer for Gene Expression Modeling. We provided the detailed T-GEM model for modeling gene–gene interactions and demonstrated its utility for gene expression-based predictions of cancer-related phenotypes, including cancer type prediction and immune cell type classification. We carefully analyzed the learning mechanism of T-GEM and showed that the first layer has broader attention while higher layers focus more on phenotype-related genes. We also showed that T-GEM’s self-attention could capture important biological functions associated with the predicted phenotypes. We further devised a method to extract the regulatory network that T-GEM learns by exploiting the attributions of self-attention weights for classifications and showed that the network hub genes were likely markers for the predicted phenotypes. 

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4. Evolution of a cis -Acting SNP That Controls Type VI Secretion in Vibrio cholerae

   https://doi.org/10.1128/mbio.00422-22  

   Ng, Siu Lung; Kammann, Sophia; Steinbach, Gabi; Hoffmann, Tobias; Yunker, Peter J.; Hammer, Brian K. (May 2022, mBio)

   Storz, Gisela (Ed.)

   ABSTRACT Mutations in regulatory mechanisms that control gene expression contribute to phenotypic diversity and thus facilitate the adaptation of microbes and other organisms to new niches. Comparative genomics can be used to infer rewiring of regulatory architecture based on large effect mutations like loss or acquisition of transcription factors but may be insufficient to identify small changes in noncoding, intergenic DNA sequence of regulatory elements that drive phenotypic divergence. In human-derived Vibrio cholerae , the response to distinct chemical cues triggers production of multiple transcription factors that can regulate the type VI secretion system (T6), a broadly distributed weapon for interbacterial competition. However, to date, the signaling network remains poorly understood because no regulatory element has been identified for the major T6 locus. Here we identify a conserved cis -acting single nucleotide polymorphism (SNP) controlling T6 transcription and activity. Sequence alignment of the T6 regulatory region from diverse V. cholerae strains revealed conservation of the SNP that we rewired to interconvert V. cholerae T6 activity between chitin-inducible and constitutive states. This study supports a model of pathogen evolution through a noncoding cis -regulatory mutation and preexisting, active transcription factors that confers a different fitness advantage to tightly regulated strains inside a human host and unfettered strains adapted to environmental niches. IMPORTANCE Organisms sense external cues with regulatory circuits that trigger the production of transcription factors, which bind specific DNA sequences at promoters (“ cis ” regulatory elements) to activate target genes. Mutations of transcription factors or their regulatory elements create phenotypic diversity, allowing exploitation of new niches. Waterborne pathogen Vibrio cholerae encodes the type VI secretion system “nanoweapon” to kill competitor cells when activated. Despite identification of several transcription factors, no regulatory element has been identified in the promoter of the major type VI locus, to date. Combining phenotypic, genetic, and genomic analysis of diverse V. cholerae strains, we discovered a single nucleotide polymorphism in the type VI promoter that switches its killing activity between a constitutive state beneficial outside hosts and an inducible state for constraint in a host. Our results support a role for noncoding DNA in adaptation of this pathogen. 

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5. Positive Selection Drives cis- regulatory Evolution Across the Threespine Stickleback Y Chromosome

   https://doi.org/10.1093/molbev/msae020  

   Shaw, Daniel E; Naftaly, Alice Shanfelter; White, Michael A (February 2024, Molecular Biology and Evolution)

   Wittkopp, Patricia (Ed.)

   Abstract Allele-specific gene expression evolves rapidly on heteromorphic sex chromosomes. Over time, the accumulation of mutations on the Y chromosome leads to widespread loss of gametolog expression, relative to the X chromosome. It remains unclear if expression evolution on degrading Y chromosomes is primarily driven by mutations that accumulate through processes of selective interference, or if positive selection can also favor the down-regulation of coding regions on the Y chromosome that contain deleterious mutations. Identifying the relative rates of cis-regulatory sequence evolution across Y chromosomes has been challenging due to the limited number of reference assemblies. The threespine stickleback (Gasterosteus aculeatus) Y chromosome is an excellent model to identify how regulatory mutations accumulate on Y chromosomes due to its intermediate state of divergence from the X chromosome. A large number of Y-linked gametologs still exist across 3 differently aged evolutionary strata to test these hypotheses. We found that putative enhancer regions on the Y chromosome exhibited elevated substitution rates and decreased polymorphism when compared to nonfunctional sites, like intergenic regions and synonymous sites. This suggests that many cis-regulatory regions are under positive selection on the Y chromosome. This divergence was correlated with X-biased gametolog expression, indicating the loss of expression from the Y chromosome may be favored by selection. Our findings provide evidence that Y-linked cis-regulatory regions exhibit signs of positive selection quickly after the suppression of recombination and allow comparisons with recent theoretical models that suggest the rapid divergence of regulatory regions may be favored to mask deleterious mutations on the Y chromosome. 

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