Biology subjects

Arrieta-Ortiz, M. L.

Publications and source records attributed to Arrieta-Ortiz, M. L..

11 recordsLinked to original sources

bioRxiv2026

Monocyte-amplified transcriptional signatures of human diseases

Blood-based biomarkers discovered by machine learning often lack disease specificity and cross-population robustness for clinical applications. We describe a biomarker discovery strategy that exploits monocytes as circulating sentinels to amplify disease-perturbed signals in blood. This strategy leverages monocyteMINER, a mechanistic transcriptional regulatory network inferred from monocyte transcriptomes of 1,202 healthy individuals. As proof-of-concept, we uncovered a 31-gene atherosclerosis-perturbed network that underpins disease etiology, identifying diagnostic signatures for coronary artery disease (ARAP2, P2RY14, FKBP15) and acute myocardial infarction (SERPINA1, ASGR2). For tuberculosis (TB), monocyteMINER uncovered a 5-gene signature (MAS_TB_META5: ANKRD22, AIM2, VAMP5, GBP5, TGM2) from just 438 samples. MAS_TB_META5 outperformed 77 existing signatures across 18 cohorts (>4,400 patients, 12 countries), achieving WHO target product profile for high-sensitivity screening (including in advanced HIV patients), and predicting TB progression up to 5 years before diagnosis. Thus, our findings show that monocyteMINER offers a generalizable platform for discovering clinically actionable biomarkers for diverse diseases.

systems biology↗

bioRxiv2025

Host oxidative stress primes mycobacteria for rapid antibiotic resistance evolution

The rapid emergence of multidrug-resistant Mycobacterium tuberculosis (Mtb) threatens global TB control, yet the mechanisms enabling rapid evolution of drug resistance in Mtb remain poorly understood. Here we reveal that pre-existing mutations in oxidative stress response genes create permissive genomic backgrounds that accelerate high-level isoniazid resistance (INHR) without fitness costs, challenging the paradigm that resistance mutations always precede their fitness compensatory adaptations. Using M. smegmatis mc2155 (Msm) as a model, we show that brief exposure to sublethal INH (2x IC50) enriches for "low-level resistance and tolerance" (LLRT) mutants in a single step. These LLRT mutants, particularly those with ohrR loss-of-function mutations, acquire high-level resistance (> 500x IC50) at 6-fold higher rates than wildtype, primarily through otherwise-deleterious mycothiol biosynthesis mutations that become tolerable in the oxidative stress-buffered background. Crucially, we demonstrate that sublethal oxidative stress alone, mimicking host immune pressure, nearly tripled the rate of INH resistance evolution in Msm. Bayesian analysis of 1,578 clinical Mtb isolates from Vietnam confirmed that mutations in oxidative stress response genes were significantly associated with the emergence of INHR strains (p-value = 1.09x10-7). Independently, reanalysis of genome-wide CRISPRi screens revealed that the OSR network and high Bayes probability genes are functionally associated with treatment escape and survival with multiple antibiotics, including isoniazid, rifampicin, ethambutol, bedaquiline, vancomycin, clarithromycin, linezolid, and streptomycin. Our findings that host-imposed oxidative stress and inadequate drug penetration may synergistically prime Mtb populations for rapid resistance evolution suggest that targeting pre-resistance mechanisms, such as oxidative stress defenses, could help slow the emergence of antibiotic resistance in tuberculosis.

microbiology↗

bioRxiv2023

Genetic manipulation of candidate phyla radiation bacteria provides functional insights into microbial dark matter

The study of bacteria has yielded fundamental insights into cellular biology and physiology, biotechnological advances and many therapeutics. Yet due to a lack of suitable tools, the significant portion of bacterial diversity held within the candidate phyla radiation (CPR) remains inaccessible to such pursuits. Here we show that CPR bacteria belonging to the phylum Saccharibacteria exhibit natural competence. We exploit this property to develop methods for their genetic manipulation, including the insertion of heterologous sequences and the construction of targeted gene deletions. Imaging of fluorescent protein-labeled Saccharibacteria provides high spatiotemporal resolution of phenomena accompanying epibiotic growth and a transposon insertion sequencing genome-wide screen reveals the contribution of enigmatic Saccharibacterial genes to growth on their Actinobacteria hosts. Finally, we leverage metagenomic data to provide cutting-edge protein structure-based bioinformatic resources that support the strain Southlakia epibionticum and its corresponding host, Actinomyces israelii, as a model system for unlocking the molecular underpinnings of the epibiotic lifestyle.

microbiology↗

bioRxiv2021

MtrA regulation of essential peptidoglycan cleavage in Mycobacterium tuberculosis during infection

The success of Mycobacterium tuberculosis (Mtb) is largely due to its ability to withstand multiple stresses encountered in the host. Here, we present a data-driven model that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. The model captures the genome-wide distribution of cis-acting gene regulatory elements and the conditional influences of transcription factors at those elements to elicit environment-specific responses. Analysis of transcriptional responses that may be essential for Mtb to survive acidic stress within the maturing macrophage, identified regulatory control by the MtrAB two-component signal system. Using genome-wide transcriptomics as well as imaging studies, we have characterized the MtrAB circuit by tunable CRISPRi knockdown in both Mtb and the non-pathogenic organism, M. smegmatis (Msm). These experiments validated the essentiality of MtrA in Mtb, but not Msm. We identified that MtrA regulates multiple enzymes that cleave cell wall peptidoglycan and is required for efficient cell division. Moreover, our results suggest that peptidoglycan cleavage, regulated by MtrA, is necessary for Mtb to survive intracellular stress. Further, we present MtrA as an attractive drug target, as even weak repression of mtrA results in loss of Mtb viability and completely clears the bacteria with low-dose isoniazid or rifampicin treatment.

systems biology↗

bioRxiv2021

Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks of Mycobacterium tuberculosis

The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states, underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME (Phenotype of Regulatory influences Integrated with Metabolism and Environment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.

systems biology↗

bioRxiv2020

Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile

Though Clostridioides difficile is among the most studied anaerobes, the interplay of metabolism and regulation that underlies its ability to colonize the human gut is unknown. We have compiled public resources into three models and a portal to support comprehensive systems analysis of C. difficile. First, by leveraging 151 transcriptomes from 11 studies we generated a regulatory model (EGRIN) that organizes 90% of C. difficile genes into 297 high quality conditional co-regulation modules. EGRIN predictions, validated with independent datasets, recapitulated and extended regulons of key transcription factors, implicating new genes for sporulation, carbohydrate transport and metabolism. Second, by advancing a metabolic model, we discovered that 15 amino acids, diverse carbohydrates, and 10 metabolic genes are essential for C. difficile growth within an intestinal environment. Finally, by integrating EGRIN with the metabolic model, we developed a PRIME model that revealed unprecedented insights into combinatorial control of essential processes for in vivo colonization of C. difficile and its interactions with commensals. We have developed an interactive web portal (http://networks.systemsbiology.net/cdiff-portal/) to disseminate all data, algorithms, and models to support collaborative systems analyses of C. difficile.

systems biology↗

bioRxiv2020

Disrupting the ArcA regulatory network increases tetracycline susceptibility of TetR Escherichia coli

There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline resistant (TetR) Escherichia coli undergoes global transcriptional and metabolic remodeling, including down-regulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Targeted knockout of ArcA, identified by network analysis as a master regulator among 25 transcription factors of this new compensatory physiological state, significantly increased the susceptibility of TetR E. coli to tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain also synergistically re-sensitized TetR E. coli to tetracycline. The potentiating effect of sertraline was eliminated upon knocking out arcA, demonstrating that the mechanism of synergy was through action of sertraline on the tetracycline-induced ArcA network in the TetR strain. Our findings demonstrate that targeting mechanistic drivers of compensatory physiological states could be a generalizable strategy to re-sensitize AMR pathogens to lost antibiotics.

systems biology↗

bioRxiv2020

A system-level gene regulatory network model for Plasmodium falciparum

Many of the gene regulatory processes of Plasmodium falciparum, the deadliest malaria parasite, remain poorly understood. To develop a comprehensive guide for exploring this organisms gene regulatory network, we generated a system-level model of Plasmodium falciparum gene regulation using a well-validated, machine-learning approach for predicting interactions between transcription regulators and their targets. The resulting network accurately predicts expression levels of transcriptionally coherent gene regulatory programs in independent transcriptomic data sets from parasites collected by different research groups in diverse laboratory and field settings. Thus, our results indicate that our gene regulatory model has predictive power and utility as a hypothesis-generating tool for illuminating clinically relevant gene regulatory mechanisms within Plasmodium falciparum. Using the set of regulatory programs we identified, we also investigated correlates of artemisinin resistance based on gene expression coherence. We report that resistance is associated with incoherent expression across many regulatory programs, including those controlling genes associated with erythrocyte-host engagement. These results suggest that parasite populations with reduced artemisinin sensitivity are more transcriptionally heterogenous. This pattern is consistent with a model where the parasite utilizes bet-hedging strategies to diversify the population, rendering a subpopulation more able to navigate drug treatment.

systems biology↗

bioRxiv2019

Antibody-Recruiting Protein-Catalyzed Capture Agents to Combat Antibiotic-Resistant Bacteria

Antibiotic resistant infections are projected to cause over 10 million deaths by 2050, yet the development of new antibiotics has slowed. This points to an urgent need for methodologies for the rapid development of antibiotics against emerging drug resistant pathogens. We report on a generalizable combined computational and synthetic approach, called antibody-recruiting protein-catalyzed capture agents (AR-PCCs), to address this challenge. We applied the combinatorial PCC technology to identify macrocyclic peptide ligands against highly conserved surface protein epitopes of carbapenem-resistant Klebsiella pneumoniae, an opportunistic gram-negative pathogen with drug resistant strains. Multi-omic data combined with bioinformatic analyses identified epitopes of the highly expressed MrkA surface protein of K. pneumoniae for targeting in PCC screens. The top-performing ligand exhibited high-affinity (EC50[~]50 nM) to full-length MrkA, and selectively bound to MrkA-expressing K. pneumoniae, but not to other pathogenic bacterial species. AR-PCCs conjugated with immunogens promoted antibody recruitment to K. pneumoniae, leading to phagocytosis and phagocytic killing by macrophages. The rapid development of this highly targeted antibiotic implies that the integrated computational and synthetic toolkit described here can be used for the accelerated production of antibiotics against drug resistant bacteria.

bioengineering↗

bioRxiv2019

Intricate genetic programs controlling dormancy in Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB), responsible for the deadliest infectious disease worldwide, displays the remarkable ability to transition in and out of dormancy, a hallmark of the pathogens capacity to evade the immune system and opportunistically exploit immunocompromised individuals. Uncovering the gene regulatory programs that underlie the dramatic phenotypic shifts in MTB during disease latency and reactivation has posed an extraordinary challenge. We developed a novel experimental system to precisely control dissolved oxygen levels in MTB cultures in order to capture the chain of transcriptional events that unfold as MTB transitions into and out of hypoxia-induced dormancy. Using a comprehensive genome-wide transcription factor binding location map and insights from network topology analysis, we identified regulatory circuits that deterministically drive sequential transitions across six transcriptionally and functionally distinct states encompassing more than three-fifths of the MTB genome. The architecture of the genetic programs explains the transcriptional dynamics underlying synchronous entry of cells into a dormant state that is primed to infect the host upon encountering favorable conditions.\n\nOne Sentence SummaryHigh-resolution transcriptional time-course reveals six-state genetic program that enables MTB to enter and exit hypoxia-induced dormancy.

systems biology↗

bioRxiv2019

Inference of bacterial small RNA regulatory networks and integration with transcription factor driven regulatory networks

Small non-coding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect messenger RNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional datasets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines. We comprehensively assess the accuracy of inferred sRNA regulons using available experimental data. We uncover 30 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Our findings expand the role of sRNAs in the regulation of chemotaxis, oxidation-reduction processes, galactose intake, and generation of pyruvate. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa and Bacillus subtilis. Overall, our strategy generates novel insights into the functional implications of sRNA regulation in multiple bacterial species.\n\nIMPORTANCEIndividual bacterial genomes can have dozens of small non-coding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false positives. Due to its data-driven nature, our method emerges as an ideal strategy to identify biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

systems biology↗