Regulatory interactions mediated by transcription factors (TFs) make up complex networks that control cellular behavior. Fully understanding these gene regulatory networks (GRNs) offers greater insight into the consequences of disease-causing perturbations than can be achieved by studying single TF binding events in isolation. Chromosomal translocations of the lysine methyltransferase 2A ( KMT2A ) gene produce KMT2A fusion proteins such as KMT2A-AFF1 (previously MLL-AF4), causing poor prognosis acute lymphoblastic leukemias (ALLs) that sometimes relapse as acute myeloid leukemias (AMLs). KMT2A-AFF1 drives leukemogenesis through direct binding and inducing the aberrant overexpression of key genes, such as the anti-apoptotic factor BCL2 and the proto-oncogene MYC . However, studying direct binding alone does not incorporate possible network-generated regulatory outputs, including the indirect induction of gene repression. To better understand the KMT2A-AFF1-driven regulatory landscape, we integrated ChIP-seq, patient RNA-seq, and CRISPR essentiality screens to generate a model GRN. This GRN identified several key transcription factors such as RUNX1 that regulate target genes downstream of KMT2A-AFF1 using feed-forward loop (FFL) and cascade motifs. A core set of nodes are present in both ALL and AML, and CRISPR screening revealed several factors that help mediate response to the drug venetoclax. Using our GRN, we then identified a KMT2A-AFF1:RUNX1 cascade that represses CASP9 , as well as KMT2A-AFF1-driven FFLs that regulate BCL2 and MYC through combinatorial TF activity. This illustrates how our GRN can be used to better connect KMT2A-AFF1 behavior to downstream pathways that contribute to leukemogenesis, and potentially predict shifts in gene expression that mediate drug response.

Industrial BiotechnologyVol. 17, No. 4 Roundtable DiscussionThe Future of MeatModerator: Teryn Wolfe, Panelists: Eric Jenkusky, Frea Mehta, Mark Langley, and Max JamillyModerator: Teryn WolfeMeasurement Matters, Bogotá, ColombiaSearch for more papers by this author, Panelists: Eric JenkuskyCo-Founder & CEO, Matrix Meats, Columbus, OH, USASearch for more papers by this author, Frea MehtaCell Biologist, Bluu Biosciences, Berlin, GermanySearch for more papers by this author, Mark LangleyManaging Partner, Unovis Asset Management, New York, NY, USASearch for more papers by this author, and Max JamillyCo-Founder, Hoxton Farms, London, United KingdomSearch for more papers by this authorPublished Online:16 Aug 2021https://doi.org/10.1089/ind.2021.29255.twoAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 17Issue 4Aug 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Moderator: Teryn Wolfe, Panelists: Eric Jenkusky, Frea Mehta, Mark Langley, and Max Jamilly.The Future of Meat.Industrial Biotechnology.Aug 2021.178-184.http://doi.org/10.1089/ind.2021.29255.twoPublished in Volume: 17 Issue 4: August 16, 2021Online Ahead of Print:August 3, 2021PDF download

ABSTRACT Regulatory interactions mediated by transcription factors (TFs) make up complex networks that control cellular behavior. Fully understanding these gene regulatory networks (GRNs) offers greater insight into the consequences of disease-causing perturbations than studying single TF binding events in isolation. Chromosomal translocations of the Mixed Lineage Leukemia gene ( MLL ) produce MLL fusion proteins such as MLL-AF4, causing poor prognosis acute lymphoblastic leukemias (ALLs). MLL-AF4 is thought to drive leukemogenesis by directly binding to genes and inducing aberrant overexpression of key gene targets, including anti-apoptotic factors such as BCL-2. However, this model minimizes the potential for circuit generated regulatory outputs, including gene repression. To better understand the MLL-AF4 driven regulatory landscape, we integrated ChIP-seq, patient RNA-seq and CRISPR essentiality screens to generate a model GRN. This GRN identified several key transcription factors, including RUNX1, that regulate target genes using feed-forward loop and cascade motifs. We used CRISPR screening in the presence of the BCL-2 inhibitor venetoclax to identify functional impacts on apoptosis. This identified an MLL-AF4:RUNX1 cascade that represses CASP9, perturbation of which disrupts venetoclax induced apoptosis. This illustrates how our GRN can be used to better understand potential mechanisms of drug resistance acquisition. Graphical abstract caption A network model of the MLL-AF4 regulatory landscape identifies feed-forward loop and cascade motifs. Functional screening using CRISPR and venetoclax identified an MLL-AF4:RUNX1: CASP9 repressive cascade that impairs drug-induced cell death.

Spatial/temporal control of Cas9 guide RNA expression could considerably expand the utility of CRISPR-based technologies. Current approaches based on tRNA processing offer a promising strategy but suffer from high background. Here, to address this limitation, we present a screening platform which allows simultaneous measurements of the promoter strength, 5′, and 3′ processing efficiencies across a library of tRNA variants. This analysis reveals that the sequence determinants underlying these activities, while overlapping, are dissociable. Rational design based on the ensuing principles allowed us to engineer an improved tRNA scaffold that enables highly specific guide RNA production from a Pol-II promoter. When benchmarked against other reported systems this tRNA scaffold is superior to most alternatives, and is equivalent in function to an optimized version of the Csy4-based guide RNA release system. The results and methods described in this manuscript enable avenues of research both in genome engineering and basic tRNA biology.

Max Jamilly, a PhD student at the University of Oxford, Fintan Nagle, a cognitive neuroscientist at UCL, and Charles Ross, Chairman of the Brain Mind Forum, discuss a new era of human and machine symbiosis for artificial intelligence.

In the first of four articles on the implications of the convergence of computing, biogenetics and cognitive neuroscience, Charles Ross and Max Jamilly look at how computers can help us extend our individual powers of learning and understanding and move us towards prosthetic brains.

The third in a series of four articles on the implications of the convergence of computing, biogenetics and cognitive neuroscience by Charles Ross and Max Jamilly looks at the new field of biological computing and swapping microprocessors for microbiology.

The second in a series of four articles on the implications of the convergence of computing, biogenetics and cognitive neuroscience by Charles Ross and Max Jamilly looks at communication between the brain and computers and how this can expand human abilities.