Biology subjects

Wong, P.

Publications and source records attributed to Wong, P..

At least 19 recordsLinked to original sources

bioRxiv2026

The Smac mimetic drug Xevinapant sensitizes triple negative breast cancer to radiotherapy-induced cell death in a TNFα dependent manner

Purpose: There is an unmet need in the treatment of triple-negative breast cancer (TNBC), as recurrence rates remain high; thus, novel treatment strategies are warranted to improve outcomes for patients. Smac mimetics (SMs) are small molecules that inhibit the inhibitors of apoptosis. In this study, we investigated the properties of the SM Xevinapant (AT406) as a radiosensitizer in TNBC. Methods: TNBC cell lines were treated with Xevinapant (AT406) in the presence or absence of ionizing RT and cell viability and apoptosis measured in vitro. Clonogenic assays were performed to confirm radiosensitization. We also assessed xevinapant radiosensitization in the presence of the Tumor Necrosis Factor (TNF) cytokine, which has been shown to enhance SM-induced apoptosis. Radiosensitization mechanisms were further validated by western blotting, flow cytometry and cell cycle analysis. Results: We show that Xevinapant increased cell death in a panel of irradiated TNBC cell lines. In clonogenic assays, Xevinapant sensitized MDA-MB-231 cells to RT-induced cell death, and this was significantly enhanced in the presence of TNF, with a dose-enhancing factor (DEF) of 1.57. We also show that Xevinapant radiosensitization is mediated by TNF-dependent apoptosis. Conclusions: Xevinapant is a radiosensitizer that enhances RT-induced cell death in TNBC cells, and TNF is central to this process through activation of apoptosis. These results are promising for combining RT with Xevinapant to improve local control in TNBC.

cancer biology↗

bioRxiv2026

Direct interactions of CEACAM1 and CD36 with LPS and each other

Micellar lipopolysaccharide (mLPS), a major shed product of gram-negative bacteria in the intestine, may result in TLR4-mediated sepsis in the circulation. CEACAM1 and CD36, expressed on both epithelia and immune cells, regulate TLR4 signaling and inflammation, suggesting a shared role in innate immunity. Furthermore, CEACAM1 associates with CD36 on hepatocytes, regulating lipid storage and bile acids (BAs) in the liver, where LPS detoxification occurs. Using E. coli mLPS-Ra as a model and soluble forms sCEACAM1 or sCD36, we assessed their direct binding to mLPS by SPR, SEC, and TEM. SPR derived steady state affinity constants (KD= 1.47 x 10-6 M and KD= 2.23 x 10-6 M) were obtained for mLPS-Ra binding to sCEACAM1 and sCD36, respectively. mLPS-Ra binding to sCEACAM1 and sCD36 was reduced by bile acids in order of sodium deoxycholate > sodium cholate. mLPS exhibited dose dependent binding to sCEACAM and sCD36 by SEC and the isolated complexes formed well defined micelles on TEM. A KD of 5.28 x 10-8 M was obtained for sCEACAM1 binding to immobilized sCD36. Antibody-based-proximity ligation assays (PLA) demonstrated the association of the ectodomains of CEACAM1 and CD36 on hepatic (P< 0.001) and HEK cells (P< 0.018), while biotin-based PLA demonstrated association of the cytoplasmic domains of CEACAM1 and a CD36-BioID2 fusion protein in HEK cells. Alpha Fold predicted CD36 binding in cis to CEACAM1 in a membrane model, in agreement with the results of the PLAs. Based on the SPR, SEC and TEM binding studies, CEACAM1 and CD36 share a cooperative mLPS binding function distinct from their regulation of TLR4 binding.

biochemistry↗

bioRxiv2025

Neuronal silencing through depolarization block underlies odor discrimination

The olfactory system enables animals to distinguish between attractive and aversive odor sources through the perception of odorant molecules. A fundamental challenge is maintaining stable odor identity perception despite fluctuating stimulus intensities during navigation. Using the numerically simple olfactory system of the Drosophila larva, we combined a quantitative odor discrimination assay, electrophysiology, computational modelling, and ectopic expression of odorant receptors to study the encoding of odor identity. We found that depolarization block, which silences olfactory sensory neurons (OSNs) at odor concentrations 3-4 log units above the detection threshold, enables odor discrimination by creating distinct patterns of OSN activity. In a minimal system with two functional OSNs, larvae discriminate between odors when depolarization block creates differential OSN silencing but fail to distinguish odors producing qualitatively similar activity patterns in both OSNs. This principle extends to odor mixtures and the fully functional olfactory system, where the high affinity of the OR42b odorant receptor for one of the test odors enables discrimination regardless of which OSN expresses OR42b. Our results indicate that depolarization block serves as an integral feature of olfactory coding alongside combinatorial OSN activation. Selective silencing of high-affinity OSNs creates distinct neural representations and enables discrimination between odors that would otherwise produce similar activity patterns. This mechanism appears evolutionarily conserved across sensory systems, from olfactory sensory neurons to photoreceptors, suggesting that selective neuronal silencing serves as a mechanism for maintaining distinct sensory representations across stimulus intensities.

neuroscience↗

bioRxiv2025

Automatic binding of basic sensory features requires consciousness

Conscious awareness requires establishing coherent percepts. Yet, whether consciousness is necessary for initiating the integration of basic sensory features remains unclear. Competing theories implicate distinct functional regimes of consciousness in the process of feature binding and creating conscious percepts. We used a novel multi-feature oddball paradigm with intracranial stereo-electroencephalography (sEEG) recordings in awake and anesthetized states to investigate the functional boundary of consciousness. In the awake state, the auditory attributes of loudness and tone, as well as the binding of the two features, were automatically encoded without attention to the stimuli in a functionally localized sensory cortical network. In the anesthetized state, the cortical registration of single attributes was preserved, whereas the binding was abolished. Moreover, anesthesia mostly influenced later cortical processes after stimuli offset. These results reveal the borderline of exertion of consciousness between the encoding and manipulation of basic sensory features in local cortical circuits - the functional boundary of consciousness constrains the feedforward binding and recurrent process directly at local rather than global level computations.

neuroscience↗

bioRxiv2025

Hypoxic-Core (HyCo) Spheroids Recapitulate Hallmarks of Clinical Hypoxia: A Simple Chip-Based Method for Translational Oncology

Hypoxia influences the biology and response of cancers. No user-friendly device allows the study of hypoxia on highly-controlled clinically-relevant tumor models. Here, we describe how hypoxic-core (HyCo) spheroids generated using our unique non-perfused microfluidic device recapitulate key clinical hallmarks of hypoxia in vitro. Our PDMS-made system can generate up to 240 spheroids naturally exhibiting a diffusion-driven hypoxic core in only 4 days, here from two sarcoma cell lines. Compared to smaller normoxic spheroids from the same cell lines, known hypoxia-related genes are upregulated in HyCo spheroids. In addition, HyCo spheroids display hallmark hypoxia-induced resistance to radiotherapy and chemotherapy, along with increased invasiveness. Finally, to demonstrate applications of our HyCo spheroids and on-chip framework in drug development, we used our HyCo-derived gene expression dataset to select a drug candidate (diethyl-pythiDC) and confirmed its effect on spheroid invasiveness. Our results suggest that HyCo spheroids can be efficiently used as translational tools to integrate hypoxia in cancer research, without complex workflows or setups. TeaserOn-chip spheroids with a natural hypoxic-core emulate clinical cancer hypoxia hallmarks essential for novel therapy development.

cancer biology↗

bioRxiv2025

The impact of perturbation intensity schedule on improvements in reactive balance control in young adults: an experimental study

BackgroundReactive balance training (RBT) uses unanticipated perturbations to improve balance reactions and prevent falls. Intensity and predictability of perturbations may affect the generalizability of RBT, but their relative contributions remain unclear. This study aimed to compare the effects of three RBT schedules with differing intensity and predictability on participants balance recovery following untrained perturbations, and to determine perceived difficulty and challenge of the training schedules. MethodsParticipants were 36 healthy young adults (20-35 years). Participants were randomly assigned to one of three RBT intensity schedules (fixed high intensity, low-to-high intensity, and variable intensity). Training took place on a motion platform that delivered perturbations in varying directions (forward, backward, left, and right) and intensities across five trial blocks. Balance reactions (number of recovery steps) pre- and post-training, and electrodermal responses, and perceived difficulty during training were collected. Statistical analyses compared post-training outcomes between training groups, controlling for the baseline value. ResultsParticipants took fewer steps and decreased the proportion of multi-step reactions pre- to post-training (p<0.0001), with no significant between-group differences. Perceived exertion decreased significantly across training blocks in the fixed-high group and increased significantly in the low-to-high group. Electrodermal responses declined across all groups between training blocks 1 and 3 (p=0.017). ConclusionsImprovements in step reactions to untrained perturbations did not differ between training groups, highlighting the importance of multi-directional variability over specific intensity schedules for enhancing balance recovery.

neuroscience↗

bioRxiv2024

Serum Amyloid P Secreted by Bone Marrow Adipocytes Drives Skeletal Amyloidosis

The accumulation of amyloid fibrils has been identified in tissues outside the brain, yet little is understood about the formation of extracerebral amyloidosis and its impact on the aging process of these organs. Here, we demonstrate that both transgenic mice modeling Alzheimers disease (AD) and naturally aging mice exhibit accumulated senescent bone marrow adipocytes (BMAds), accompanied by amyloid deposits surrounding the BMAds. Senescent BMAds acquire a secretory phenotype, resulting in a marked increase in the secretion of serum amyloid P component (SAP), also known as pentraxin 2 (PTX2). SAP/PTX2 colocalizes with amyloid deposits around senescent BMAds in vivo and is sufficient to promote the formation of insoluble amyloid deposits from soluble A{beta} peptides in in vitro and ex vivo 3D BMAd-based culture experiments. Additionally, Combined treatment with SAP/PTX2 and A{beta} peptides promotes osteoclastogenesis but inhibits osteoblastogenesis of the precursor cells. Transplantation of senescent BMAds into the bone marrow cavity of healthy young mice is sufficient to induce bone loss. Finally, pharmacological depletion of SAP/PTX2 from aged mice abolishes bone marrow amyloid deposition and effectively rescues the low bone mass phenotype. Thus, senescent BMAds, through the secretion of SAP/PTX2, contribute to the age-associated development of skeletal amyloidosis and resultant bone deficits.

cell biology↗

bioRxiv2024

Expression of a single inhibitory Ly49 receptor is sufficient to license NK cells for effector functions

Natural killer (NK) cells recognize target cells through germline-encoded activation and inhibitory receptors enabling effective immunity against viruses and cancer. The Ly49 receptor family in the mouse and killer immunoglobin-like receptor family in humans play a central role in NK cell immunity through recognition of MHC class I and related molecules. Functionally, these receptor families are involved in licensing and rejection of MHC-I-deficient cells through missing-self. The Ly49 family is highly polymorphic, making it challenging to detail the contributions of individual Ly49 receptors to NK cell function. Herein, we showed mice lacking expression of all Ly49s were unable to reject missing-self target cells in vivo, were defective in NK cell licensing, and displayed lower KLRG1 on the surface of NK cells. Expression of Ly49A alone on a H-2Dd background restored missing-self target cell rejection, NK cell licensing, and NK cell KLRG1 expression. Thus, a single inhibitory Ly49 receptor is sufficient to license NK cells and mediate missing-self in vivo.

immunology↗

bioRxiv2023

Quantification of circulating tumor DNA using deep learning

Quantification of circulating tumor DNA (ctDNA) levels in blood enables non-invasive surveillance of cancer progression. Fragle is an ultra-fast deep learning-based method for ctDNA quantification directly from cell-free DNA fragment length profiles. We developed Fragle using low-pass whole genome sequence (lpWGS) data from multiple cancer types and healthy control cohorts, demonstrating high accuracy, and improved lower limit of detection in independent cohorts as compared to existing tumor-naive methods. Uniquely, Fragle is also compatible with targeted sequencing data, exhibiting high accuracy across both research and commercial targeted gene panels. We used this method to study longitudinal plasma samples from colorectal cancer patients, identifying strong concordance of ctDNA dynamics and treatment response. Furthermore, prediction of minimal residual disease in resected lung cancer patients demonstrated significant risk stratification beyond a tumor-naive gene panel. Overall, Fragle is a versatile, fast, and accurate method for ctDNA quantification with potential for broad clinical utility.

bioinformatics↗

bioRxiv2023

Computational principles of adaptive multisensory combination in the Drosophila larva

Many sensory systems have evolved to optimally combine signals from multiple sensory modalities to improve perception. While theories have been proposed to explain how this process is accomplished through probabilistic inference using large neural populations in vertebrates, how animals with dramatically smaller nervous systems such as the Drosophila melanogaster larva achieve multisensory combination remains elusive. Here, we systematically characterize larval navigation in different configurations of odor and temperature gradients with optogenetically-controlled noise. Using a data-driven agent-based model, we find that larvae adapt to the reliability of individual sensory signals, and in some cases minimize the variance of the combined signal. Besides firmly establishing that probabilistic inference directs natural orientation behaviors in the Drosophila larva, our results indicate that the exact mechanism underlying the combination of sensory information may be modality-dependent. By underscoring that probabilistic inference is inherent to insect nervous systems, our work opens the way for studying its neural implementation.

neuroscience↗

bioRxiv2022

Treatment with Tumor-Treating Fields (TTFields) Suppresses Intercellular Tunneling Nanotube Formation In Vitro and Upregulates Immuno-Oncologic Biomarkers In Vivo in Malignant Mesothelioma

Intercellular communication is critical for the development of invasive cancers. Multiple forms of intercellular communication have been well characterized, involving diffusible soluble factors or contact-dependent channels for immediately adjacent cells. Over the past 1-2 decades, the emergence of a unique form of F-actin-based cellular protrusion known as tunneling nanotubes (TNTs) has filled the niche of long-range cell-contact dependent intercellular communication that facilitates cell growth, differentiation, and in the case of invasive cancer phenotypes, a more chemoresistant phenotype. The cellular machinery of TNT-mediated transport is an area of active investigation, and microtubules have been implicated in this process as they are in other membranous protrusions. Tumor-Treating Fields (TTFields) therapy is a novel therapeutic strategy in clinical use for patients with advanced cancers, based on the principle of using low-intensity alternating electric fields to disrupt microtubules in cancer cells undergoing mitosis. Other mechanisms of action have also been demonstrated. In this study, we investigated the effects of TTFields on TNTs in malignant pleural mesothelioma (MPM) in vitro and also on the spatial transcriptomic landscape in vivo. We found that applying TTFields at 1.0 V/cm significantly suppressed TNT formation in a biphasic MPM cell line (MSTO-211H), but not in sarcomatoid MPM (VAMT). At these parameters, TTFields significantly reduced cell count in MSTO-211H, but did not significantly alter intercellular transport of mitochondria via intact TNTs. To understand how TTFields may impact expression of genes with known involvement to TNT formation and overall tumor growth, we performed spatial genomic assessment of TTFields-treated tumors from an in vivo animal model of MPM, and detected upregulation of immuno-oncologic biomarkers with simultaneous downregulation of pathways associated with cell hyperproliferation, invasion, and other critical regulators of oncogenic growth. Several molecular classes and pathways coincide with markers that we and others have found to be differentially expressed in cancer cell TNTs, including MPM specifically. In this study, we report novel cellular and molecular effects of TTFields in relation to tumor communication networks enabled by TNTs and related molecular pathways. These results position TNTs as potential therapeutic targets for TTFields-directed cancer treatment strategies; and also identify the ability of TTFields to potentially remodel the tumor microenvironment, thus enhancing response to immunotherapeutic drugs.

cancer biology↗

bioRxiv2022

Long Noncoding RNA MALAT1 is Dynamically Regulated in Leader Cells during Collective Cancer Invasion

Cancer cells invade collectively with leader-follower organization. However, how leader cells are regulated during the dynamic invasion process remains poorly understood. Using a FRET nanobiosensor that tracks lncRNA dynamics in live single cells, we monitored the spatiotemporal distribution of lncRNA during collective cancer invasion. We show that lncRNA MALAT1 is dynamically regulated in the invading fronts of cancer cells and patient-derived organoids. The abundance, diffusivity, and distribution of MALAT1 transcripts are distinct between leader and follower cells. MALAT1 expression increases when a cell acquires the leader cell role and decreases when the migration process stops. Transient knockdown of MALAT1 prevents the formation of leader cells and abolishes the migration of cancer cells. Taken together, our single cell analysis suggests MALAT1 dynamically regulates leader cells during collective cancer invasion.

cancer biology↗

bioRxiv2021

Intratumoral Heterogeneity Promotes Collective Cancer Invasion Through NOTCH1 Variation

Cellular and molecular heterogeneity within tumors has long been associated with the progression of cancer to an aggressive phenotype and a poor prognosis. However, how such intratumoral heterogeneity contributes to the invasiveness of cancer is largely unknown. Here, using a multidisciplinary approach, we investigate the interaction between molecular subtypes within bladder microtumors and the corresponding effects on their invasiveness. Our results reveal heterogeneous microtumors formed by multiple molecular subtypes possess enhanced invasiveness compared to individual cells, even when both cells are not invasive individually. To examine the molecular mechanism of intratumoral heterogeneity mediated invasiveness, live single cell biosensing, RNA interference, and CRISPR-Cas9 gene editing approaches were applied to investigate and control the composition of the microtumors. An agent-based computational model was also developed to evaluate the influence of NOTCH1 variation on DLL4 expression within a microtumor. The data indicate that variation in NOTCH1 expression can lead to upregulation of DLL4 expression within the microtumor and enhancement of microtumor invasiveness. Overall, our results reveal a novel mechanism of heterogeneity mediated invasiveness through intratumoral variation of gene expression. Summary statementThis study reveals a mechanism that Notch1 variation, instead of the average value, promotes the invasiveness of microtumor, providing a link between intratumoral heterogeneity and collective cancer invasion.

cancer biology↗

bioRxiv2021

Generalizable predictive modeling of semantic processing ability from functional brain connectivity

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating meaningful and conceptual information. Neuroimaging studies of SP typically collapse data from many subjects, but both its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural organizations contribute to the individual differences in SP behaviors. Here we aim to identify the neural signatures underlying SP variabilities by analyzing individual functional connectivity (FC) patterns based on a large-sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two-stage predictive modeling approach to build an internally cross-validated model and to test the models generalizability with unseen data from different HCP sub-populations and task states as well as other out-of-sample datasets that are independent of the HCP. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five semantic tasks. This cross-validated predictive model can be used to predict unseen HCP data. The model generalizability was enhanced with FCs in language tasks than resting state and other task states and was better for females than males. The model constructed from the HCP dataset can be generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out-of-sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education and improve intervention practice for patients with SP and language deficits.

neuroscience↗

bioRxiv2021

Nrf2 regulates collective cancer migration by modulating the hybrid epithelial/mesenchymal phenotype

Hybrid epithelial/mesenchymal cells (E/M) are key players in aggressive cancer metastasis. It remains a challenge to understand how these cell states, which are mostly non-existent in healthy tissue, become stable phenotypes participating in collective cancer migration. The transcription factor Nrf2, which is associated with tumor progression and resistance to therapy, appears to be central to this process. Here, using a combination of single cell biosensors, immunocytochemistry, and computational modeling, we show that Nrf2 functions as a phenotypic stability factor for hybrid E/M cells by inhibiting a complete epithelial-mesenchymal transition (EMT) during collective cancer migration. We also demonstrate that Nrf2 and EMT signaling are spatially coordinated near the migrating front. In particular, computational analysis of an Nrf2-EMT-Notch network and experimental modulation of Nrf2 by pharmacological treatment or CRISPR/Cas9 gene editing reveal that Nrf2 stabilizes a hybrid E/M phenotype which is maximally observed in the interior region immediately behind the leading edge. We further demonstrate that the Nrf2-EMT-Notch network enhances Dll4 and Jagged1 expression near the leading edge, which correlates with the formation of leader cells and protruding tips. Altogether, our results provide direct evidence that Nrf2 acts as a phenotypic stability factor in restricting complete EMT and plays an important role in coordinating collective cancer migration.

cancer biology↗

bioRxiv2020

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

1The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a continuous coarse graining process called diffusion condensation, Multiscale PHATE creates a tree of data granularities that can be cut at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hi CD66blo neutrophil and IFN{gamma}+GranzymeB+ Th17 cell responses enriched in patients who die. Further, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

bioinformatics↗

bioRxiv2020

Generating hard-to-obtain information from easy-to-obtain information: applications in drug discovery and clinical inference

In many important contexts involving measurements of biological entities, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathered on some of the biological samples. For example, in the context of drug discovery, measurements like the chemical structure of a drug are EI, while measurements of the transcriptome of a cell population perturbed with the drug is HI. In the clinical context, basic health monitoring is EI because it is already being captured as part of other processes, while cellular measurements like flow cytometry or even ultimate patient outcome are HI. We propose building a model to make probabilistic predictions of HI from EI on the samples that have both kinds of measurements, which will allow us to generalize and predict the HI on a large set of samples from just the EI. To accomplish this, we present a conditional Generative Adversarial Network (cGAN) framework we call the Feature Mapping GAN (FMGAN). By using the EI as conditions to map to the HI, we demonstrate that FMGAN can accurately predict the HI, with heterogeneity in cases of distributions of HI from EI. We show that FMGAN is flexible in that it can learn rich and complex mappings from EI to HI, and can take into account manifold structure in the EI space where available. We demonstrate this in a variety of contexts including generating RNA sequencing results on cell lines subjected to drug perturbations using drug chemical structure, and generating clinical outcomes from patient lab measurements. Most notably, we are able to generate synthetic flow cytometry data from clinical variables on a cohort of COVID-19 patients--effectively describing their immune response in great detail, and showcasing the power of generating expensive FACS data from ubiquitously available patient monitoring data. Bigger PictureMany experiments face a trade-off between gathering easy-to-collect information on many samples or hard-to-collect information on a smaller number of small due to costs in terms of both money and time. We demonstrate that a mapping between the easy-to-collect and hard-to-collect information can be trained as a conditional GAN from a subset of samples with both measured. With our conditional GAN model known as Feature-Mapping GAN (FMGAN), the results of expensive experiments can be predicted, saving on the costs of actually performing the experiment. This can have major impact in many settinsg. We study two example settings. First, in the field of pharmaceutical drug discovery early phase pharmaceutical experiments require casting a wide net to find a few potential leads to follow. In the long term, development pipelines can be re-designed to specifically utilize FMGAN in an optimal way to accelerate the process of drug discovery. FMGAN can also have a major impact in clinical setting, where routinely measured variables like blood pressure or heart rate can be used to predict important health outcomes and therefore deciding the best course of treatment.

bioinformatics↗

bioRxiv2020

From the Andes to the desert: First characterization of bacterial communities in the Rimac river, the main source of water for Lima, Peru

BackgroundThe Rimac river is the main source of water for Lima, Perus capital megacity. The river is constantly affected by different types of contamination including mine tailings in the Andes and urban sewage in the metropolitan area. We aim to produce the first characterization of bacterial communities in the Rimac river using a 16S rRNA amplicon sequencing approach which would be useful to identify bacterial diversity and potential understudied pathogens. ResultsWe report a higher diversity in bacterial communities from the Upper and, especially, Middle Rimac compared to the Lower Rimac (Metropolitan zone). Samples were generally grouped according to their geographical location. Bacterial classes Alphaproteobacteria, Bacteroidia, Campylobacteria, Fusobacteriia, and Gammaproteobacteria were the most frequent along the river. Arcobacter cryaerophilus (Campylobacteria) was the most frequent species in the Lower Rimac while Flavobacterium succinicans (Bacteroidia) and Hypnocyclicus (Fusobacteriia) were the most predominant in the Upper Rimac. Predicted metabolic functions in the microbiota include bacterial motility, quorum sensing and xenobiotics metabolism. Additional metabolomic analyses showed the presence natural flavonoids and antibiotics in the Upper Rimac, and herbicides in the Lower Rimac. ConclusionsThe dominance in the Metropolitan area of Arcobacter cryaerophilus, an emergent pathogen associated with fecal contamination and antibiotic multiresistance, but that is not usually reported in traditional microbiological quality assessments, highlights the necessity to apply next-generation sequencing tools to improve pathogen surveillance. We believe that our study will encourage the integration of omics sciences in Peru and its application on current environmental and public health issues.

microbiology↗