Biology subjects

Tellez, A. V.

Publications and source records attributed to Tellez, A. V..

4 recordsLinked to original sources

bioRxiv2026

Oxidized TRX1 and DPP8/9 stabilize the MIA40-AIFM1 complex during intrinsic apoptosis

Mammalian cells execute diverse death programs with distinct immunological consequences. Mitochondrial outer membrane permeabilization (MOMP) drives typically immunologically silent intrinsic apoptosis by releasing cytochrome c and other proapoptotic proteins into the cytosol. Here, we show that MOMP also engages two established repressors of the pyroptosis-inducing NLRP1 inflammasome, oxidized thioredoxin-1 (oxTRX1) and catalytically active DPP8/9, in a coordinated redox and proteostasis program. Immediately after MOMP, oxTRX1 forms a disulfide with MIA40 Cys4 at the MIA40-AIFM1 interface. Subsequently, DPP8/9 cleave AK2, targeting this competing AIFM1 ligand for degradation. Biochemical reconstitution and cryo-EM reveal that oxTRX1 promotes AIFM1 dimerization and preferentially stabilizes MIA40-AIFM1 over AK2-AIFM1. Thus, redox-dependent complex assembly and selective proteolysis converge to favor MIA40-bound AIFM1 after MOMP. These findings suggest how post-MOMP remodeling could reinforce NLRP1 restraint during apoptosis and how disruption of this program could trigger pyroptosis.

cell biology↗

bioRxiv2025

Desiccation-induced DNA damage facilitates drug resistance in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is an obligate human pathogen that depends on its ability to spread from host-to-host to survive as a species. Yet, knowledge of transmission-specific traits remains lacking. Here, we report the discovery of a specific adaptive response to desiccation, a stress intrinsically linked to the generation of the aerosol droplets within which Mtb transmits. We show that desiccation inflicts oxidative damage and activates Mtbs DNA repair responses but that this repair is imperfect and results in mutations. We further show that activation of these DNA repair responses is accompanied by increased expression of the transcription-coupled repair factor, mfd, but that this expression serves to buffer the fitness cost of specific resistance-conferring mutations in rpoB, the target of the frontline drug rifampin, rather than to facilitate transcription-coupled DNA repair. Silencing mfd during aerosolization impairs survival of strains harboring the rifampin resistance allele S450L. This function is further supported by whole genome sequence data from over 50,000 clinically circulating strains. These studies indicate that Mtb has evolved transmission-specific stress responses that have enabled it to leverage desiccation-induced DNA damage as a potential source of genetic diversification and drug resistance.

microbiology↗

bioRxiv2022

Predicting enzyme substrate chemical structure with protein language models

The number of unannotated or orphan enzymes vastly outnumber those for which the chemical structure of the substrates are known. While a number of enzyme function prediction algorithms exist, these often predict Enzyme Commission (EC) numbers or enzyme family, which limits their ability to generate experimentally testable hypotheses. Here, we harness protein language models, cheminformatics, and machine learning classification techniques to accelerate the annotation of orphan enzymes by predicting their substrates chemical structural class. We use the orphan enzymes of Mycobacterium tuberculosis as a case study, focusing on two protein families that are highly abundant in its proteome: the short-chain dehydrogenase/reductases (SDRs) and the S-adenosylmethionine (SAM)-dependent methyltransferases. Training machine learning classification models that take as input the protein sequence embeddings obtained from a pre-trained, self-supervised protein language model results in excellent accuracy for a wide variety of prediction tasks. These include redox cofactor preference for SDRs; small-molecule vs. polymer (i.e. protein, DNA or RNA) substrate preference for SAM-dependent methyltransferases; as well as more detailed chemical structural predictions for the preferred substrates of both enzyme families. We then use these trained classifiers to generate predictions for the full set of unannotated SDRs and SAM-methyltransferases in the proteomes of M. tuberculosis and other mycobacteria, generating a set of biochemically testable hypotheses. Our approach can be extended and generalized to other enzyme families and organisms, and we envision it will help accelerate the annotation of a large number of orphan enzymes. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/509940v3_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1dab89forg.highwire.dtl.DTLVardef@8eec65org.highwire.dtl.DTLVardef@141ff13org.highwire.dtl.DTLVardef@1d16212_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2022

Genome-wide co-essentiality analysis in Mycobacterium tuberculosis reveals an itaconate defense enzyme module

Genome-wide random mutagenesis screens using transposon sequencing (TnSeq) have been a cornerstone of functional genetics in Mycobacterium tuberculosis (Mtb), helping to define gene essentiality across a wide range of experimental conditions. Here, we harness a recently compiled TnSeq database to identify pairwise correlations of gene essentiality profiles (i.e. co-essentiality analysis) across the Mtb genome and reveal clusters of genes with similar function. We describe selected modules identified by our pipeline, review the literature supporting their associations, and propose hypotheses about novel associations. We focus on a cluster of seven enzymes for experimental validation, characterizing it as an enzymatic arsenal that helps Mtb counter the toxic effects of itaconate, a host-derived antibacterial compound. We extend the use of these correlations to enable prediction of protein complexes by designing a virtual screen that ranks potentially interacting heterodimers from co-essential protein pairs. We envision co-essentiality analysis will help accelerate gene functional discovery in this important human pathogen.

microbiology↗