Biology subjects

Huang, K.

Publications and source records attributed to Huang, K..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Non-mutated human tau stimulates Alzheimer's disease-relevant neurodegeneration in a microglia-dependent manner

The accumulation of abnormal, non-mutated tau protein is a key pathological hallmark of Alzheimers disease (AD). Despite its strong association with disease progression, the mechanisms by which tau drives neurodegeneration in the brain remain poorly understood. Here, we selectively expressed non-mutated or mutated human microtubule-associated protein tau (hMAPT) in neurons across the brain and observed neurodegeneration in the hippocampus, especially associated with non-mutated human tau. Single-nuclei RNA sequencing confirmed a selective loss of hippocampal excitatory neurons by the wild-type tau and revealed the upregulation of neurodegeneration-related pathways in the affected populations. The accumulation of phosphorylated tau was accompanied by cellular stress in neurons and reactive gliosis in multiple brain regions. Notably, the lifelong absence of microglia significantly and differentially influenced the extent of neurodegeneration in the hippocampus and thalamus. Therefore, our study established an AD-relevant tauopathy mouse model, elucidated both neuron-intrinsic and neuron-extrinsic responses, and highlighted critical and complex roles of microglia in modulating tau-driven neurodegeneration.

neuroscience↗

bioRxiv2024

RNAGenesis: Foundation Model for Enhanced RNA Sequence Generation and Structural Insights

RNA molecules are central to gene regulation, catalysis, and molecular recognition, and offer broad opportunities for therapeutic applications. However, uncovering their complex sequence, structure, and function relationships, particularly for non-coding RNAs, remains a formidable challenge. Here, we introduce RNAGenesis, a Generalist RNA foundation model that integrates sequence representation, structural prediction, and de novo functional design within a single generative framework. Trained on diverse clustered non-coding RNAs, RNAGenesis leverages a BERT-style encoder, query-based latent compression, and a diffusion-guided decoder enhanced by inference-time alignment with gradient guidance and beam search strategies. Through comprehensive evaluations, RNAGenesis achieves state-of-the-art performance on 11 of 13 tasks in the BEACON benchmark and surpasses structure-aware models in inverse folding, 3D structure prediction, and de novo structure design. We further introduce RNATx-Bench, a dedicated benchmark for RNA therapeutics comprising over 100,000 experimentally validated sequences. RNAGenesis demonstrates strong predictive performance across ASOs, siRNAs, shRNAs, circRNAs, and untranslated region (UTR) variants. Furthermore, RNAGenesis enables functional RNA design, including aptamers targeting IGFBP3 and structurally constrained sgRNA scaffolds. Wet-lab validation confirms aptamer binding with KD values as low as 4.02 nM and up to 2.5-fold improvement in editing efficiency across CRISPR-Cas9, base editing, and prime editing systems. These results position RNAGenesis as a next-generation general-purpose RNA foundation model with broad utility for computational modeling and experimental therapeutic design.

bioinformatics↗

bioRxiv2024

Integrative analysis of transcriptome and metabolism reveals functional roles of redox homeostasis in low light and salt combined stress in Leymus chinensis

Soil salinity (Salt) is one of the major limiting factors of Leymus chinensis (named sheepgrass) performance and alters their primary metabolic process and gene expression. Salt stress could accelerate inhibitive effects concomitant with low light (LL-Salt). However, little is known about physiological and molecular mechanisms under such LL-Salt in sheepgrass. This study aims to uncover the key reprogrammed metabolic pathways induced by LL-Salt through an integrated analysis of transcriptome and metabolism. Plant seedlings were exposed to six combinations of light intensity (moderate light, ML, and LL) and NaCl concentrations (0, 50, and 200mM) for 20 days. Results suggest that the growth of sheepgrass seedlings was dramatically inhibited with severe lodging phenotype, especially when exposed to LL-Salt combined conditions. Activities of an antioxidant enzyme, catalase, were significantly increased in LL but significantly decreased in salt stress, leading to a reverse pattern for H2O2 in LL and salt stress. Transcriptome analysis reveals 4921 downregulated differentially expressed genes (DEGs) with carbon metabolism pathways were significantly enriched compared to light response without salt (ML_0 vs LL_0). There are 194 overlapped downregulated DEGs induced by two salt treatments (ML_50 vs ML_0 and ML_200 vs ML_0), where the carbon metabolism pathway was also significantly enriched. In terms of interactive effects of LL-Salt treatments, we found that there are 16 overlapped DEGs, including a phytochrome-interacting transcription factor 4 (PIF4) with upregulation in LL treatment while downregulation in salt treatment, which was validated by qPCR. Metabolism analysis confirmed that in carbon metabolism, the contents of seven differentially abundant metabolites (DAMs), including sucrose, were all downregulated in both LL and salt treatment. In contrast, serine and glycolate in the photorespiration pathway were downregulated in LL while upregulated in salt treatment. A photorespiration regulatory gene, glycolate oxidase expression, was confirmed by qPCR. Collectively, we found that serval antioxidant redox pathways, including photorespiration, GSG/GSSH redox, and ABA signaling, participated in response to LL and salt combined events and highlighted the roles of cellular redox homeostasis in LL-Salt response in sheepgrass.

plant biology↗

bioRxiv2024

Improved automated model building for cryo-EM maps using CryFold

Constructing atomic models from cryogenic electron microscopy (cryo-EM) density maps is essential for interpreting molecular mechanisms. In this study, we present CryFold, an approach for de novo model building for cryo-EM maps, leveraging recent advancements in AlphaFold2 (1) to improve the state-of-the-art method ModelAngelo (2). To accommodate the cryo-EM map information, CryFold replaces the global attention mechanism in AlphaFold2 with local attention, which is further enhanced by a novel 3D rotary position embedding. CryFold produces more complete models, reduces the resolution requirement, and accelerates the modeling. The application of CryFold to three new maps with unknown structure demonstrates its ability to accurately distinguish between paralog sequences in noisy regions, detect previously uncharacterized proteins with unknown functions, precisely compartmentalize the map to isolate non-protein components, and improve the modeling of conformational changes. A particular case includes a 104-protein complex that has been modeled within a few hours, and a minor conformational change of a single protein domain has been detected at the periphery when models from two different maps were compared. CryFold stands as an accurate method currently available for model building of proteins in cryo-EM structure determination. The source code and model parameters are available at https://github.com/SBQ-1999/CryFold.

bioinformatics↗

bioRxiv2024

Evolutionary rescue during extreme drought

Populations declining due to climate change may need to evolve to persist. While evolutionary rescue has been demonstrated in theory and the lab, its relevance to natural populations facing climate change remains unknown. Here we link rapid evolution and population dynamics in scarlet monkeyflower, Mimulus cardinalis, during an exceptional drought. We leverage whole-genome sequencing across 55 populations to identify climate-associated loci. Simultaneously we track demography and allele frequency change throughout the drought. We establish range-wide population decline during the drought, geographically variable rapid evolution, and variable population recovery that is predictable by standing genetic variation in and rapid evolution at climate-associated loci. These findings demonstrate evolutionary rescue in the wild, showing that genetic variation at adaptive, but not neutral loci, predicts population recovery.

evolutionary biology↗

bioRxiv2024

The conversion of forests to agricultural land reduced the content of soil black carbon fractions in a karst rocky desertification area of Southwest China

Understanding the effects of land use on soil black carbon (BC) is critical for correctly interpreting the role of BC in the carbon cycle of karst areas. This study investigated the distribution characteristics of organic carbon (OC), BC, char, and soot and soil physicochemical properties of the soil profiles (0-40 cm) of secondary forest, shrub, farmland, and wasteland in a rocky desertification area of Guangxi Province, a typical karst rocky desertification area in Southwest China. We used a combination of soot/char and {delta}13CBC isotope analysis to identify the source of BC of these soils. The average value of soil BC was the highest in secondary forest, followed by shrub, farmland, and wasteland in the 0-40 cm soil layer. BC had a positive correlation with OC, char, soot,soil nitrogen, phosphorus and potassium, showed a negative correlation with bulk density. In the black carbon component, char occupies a greater proportion than soot, with the heavy fraction OC exhibiting a higher concentration of BC than the light fraction OC. Conclusively, the BC content was mainly due to C3 plant burning, vehicle exhaust emissions, and fossil fuel utilization.Vegetation restoration improved the soil BC content associated with OC sequestration in the typical karst rocky desertification mountainous area of Southwest Guangxi.

ecology↗

bioRxiv2024

Neural Circuit Underlying Individual differences in Visual Escape Habituation

Emotions, like fear, are internal states enabling organisms to effectively confront environmental threats. When repeatedly exposed to predators, individuals show divergent adaptive responses. However, the neural circuit mechanisms underlying individual differences in to repeated threats remain largely unknown. Here, we identify two distinct types of visual escape--consistent escape (T1) and rapid habituation (T2) -- with unambiguous arousal states to repetitive threat stimuli. We systematically investigate distinct pathways originating from the superior colliculus (SC) and insula that project to the basolateral amygdala (BLA), with relay stations in the mediodorsal thalamus (MD) and ventral tegmental area (VTA), mediating T1 and T2 behavioral types. Additionally, we identify the MD as a critical hub integrating SC and insula inputs, projecting to the BLA and contributing to reduced arousal and attenuated defensive behaviors against looming stimuli. Our findings offer significant insights into the mechanisms of internal states, arousal modulation, and behavioral adaptability. O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/611366v1_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@164df34org.highwire.dtl.DTLVardef@17a01e6org.highwire.dtl.DTLVardef@112de44org.highwire.dtl.DTLVardef@1be6aea_HPS_FORMAT_FIGEXP M_FIG Graph Abstract C_FIG In briefWe identify two distinct visual escape behaviors--consistent escape (T1) and rapid habituation (T2)--linked to unique SC and insula pathways projecting to the BLA via MD and VTA. The MD acts as a hub integrating sensory inputs to modulate arousal and defensive responses. These findings advance our understanding of the neural mechanisms driving internal states, arousal modulation, and behavioral adaptability. Highlights[bullet] We identify two distinct visual escape behaviors with unambiguous arousal states--consistent escape (T1) and rapid habituation (T2) --to repeated looming stimuli. [bullet]The SC-VTA-BLA pathway mediated T1 behavioral type, while the SC-MD-BLA pathway mediated T2 type. [bullet]MD as a central hub integrating SC and insula inputs to modulate arousal and defensive behavioral adaptation.

neuroscience↗

bioRxiv2024

Single-cell transcriptome analysis reveals CD34 as a novel marker of human sinoatrial node pacemaker cardiomyocytes

The sinoatrial node (SAN) regulates the heart rate throughout life. Failure of this primary pacemaker results in life-threatening, slow heart rhythm. Despite its important function, the cellular and molecular composition of the human SAN is not completely resolved. Particularly, no cell surface marker to identify and isolate SAN pacemaker cells has been reported to date. Here we used single-nuclei/cell RNA sequencing of fetal and human pluripotent stem cell (hPSC)- derived SAN cells and show that the SAN consists of three subtypes of pacemaker cells, including Core SAN, SAN, and Transitional Cells. Our study identified a host of novel Core SAN markers including MYH11, BMP4, and the cell surface antigen CD34. We demonstrate that sorting for CD34+ cells from cardiac hPSC differentiations enriches for SAN cells with a functional pacemaker phenotype. This novel SAN pacemaker cell surface marker is highly valuable for future hPSC- based disease modelling, drug discovery, cell replacement therapies, as well as the delivery of therapeutics to SAN cells in vivo using antibody-drug conjugates.

cell biology↗

bioRxiv2024

Pannexin 1 crosstalk with the Hippo pathway in malignant melanoma

In this study, we explored the intricate relationship between Pannexin 1 (PANX1) and the Hippo signaling pathway effector, Yes-associated protein (YAP). Analysis of The Cancer Genome Atlas (TCGA) data revealed a significant positive correlation between PANX1 mRNA and core Hippo components, YAP, TAZ, and Hippo scaffold, IQGAP1, in invasive cutaneous melanoma and breast carcinoma. Furthermore, we demonstrated that PANX1 expression is upregulated in invasive melanoma cell lines and is associated with increased YAP protein levels. Notably, our investigations uncovered a previously unrecognized interaction between endogenous PANX1 and the Hippo scaffold protein IQGAP1 in melanoma cells. Moreover, our findings revealed that IQGAP1 exhibits differential expression in melanoma cells and plays a regulatory role in cellular morphology. Functional studies involving PANX1 knockdown provided compelling evidence that PANX1 modulates YAP protein levels and its co-transcriptional activity in both melanoma and breast carcinoma cells. Importantly, our study showcases the potential therapeutic relevance of targeting PANX1, as pharmacological inhibition of PANX1 using selective FDA-approved inhibitors or PANX1 knockdown reduced YAP abundance in melanoma cells. Furthermore, our Clariom S analysis unveiled key genes implicated in cell proliferation, such as neuroglin1 (NRG1), {beta}-galactoside binding protein, galectin-3 (LGALS3), that are affected in PANX1- deficient cells. In summary, our investigation delves into the intricate interplay between PANX1 and YAP in the context of invasive melanoma, offering valuable insights into potential therapeutic strategies for effective treatment.

cell biology↗

bioRxiv2024

Identifying drug-resistant individual cells within tumors by semi-supervised domain adaptation

The presence of pre-existing or acquired drug-resistant cells within the tumor often leads to tumor relapse and metastasis. Single-cell RNA sequencing (scRNA-seq) enables elucidation of the subtle differences in drug responsiveness among distinct cell subpopulations within tumors. A few methods have employed scRNA-seq data to predict the drug response of individual cells to date, but their performance is far from satisfactory. In this study, we propose SSDA4Drug, a semi-supervised few-shot transfer learning method for inferring drug-resistant cancer cells. SSDA4Drug extracts pharmacogenomic features from both bulk and single-cell transcriptomic data using semi-supervised adversarial domain adaptation. This allows us to transfer knowledge of drug sensitivity from bulk-level cell lines to single cells. We conduct extensive performance evaluation experiments across multiple independent scRNA-seq datasets, demonstrating SSDA4Drugs superior performance over current state-of-the-art methods. Remarkably, with only one or two labeled target-domain samples, SSDA4Drug significantly boosts the predictive performance of single-cell drug responses. Moreover, SSDA4Drug accurately recapitulates the temporally dynamic changes of drug responses during continuous drug exposure of tumor cells, and successfully identifies reversible drug-responsive states in lung cancer cells, which initially acquire resistance through drug exposure but later restore sensitivity during drug holidays. Also, our predicted drug responses consistently align with the developmental patterns of drug sensitivity observed along the evolutionary trajectory of oral squamous cell carcinoma cells. In addition, our derived SHAP values and integrated gradients effectively pinpoint the key genes involved in drug resistance in prostate cancer cells. These findings highlight the exceptional performance of our method in determining single-cell drug responses. This powerful tool holds the potential for identifying drug-resistant tumor cell subpopulations, paving the way for advancements in precision medicine and novel drug development.

bioinformatics↗

bioRxiv2024

TFE3 fusions direct an oncogenic transcriptional program that drives OXPHOS and unveils vulnerabilities in translocation renal cell carcinoma

Translocation renal cell carcinoma (tRCC) is an aggressive subtype of kidney cancer driven by TFE3 gene fusions, which act via poorly characterized downstream mechanisms. Here we report that TFE3 fusions transcriptionally rewire tRCCs toward oxidative phosphorylation (OXPHOS), contrasting with the highly glycolytic metabolism of most other renal cancers. This TFE3 fusion-driven OXPHOS program, together with heightened glutathione levels found in renal cancers, renders tRCCs sensitive to reductive stress - a metabolic stress state induced by an imbalance of reducing equivalents. Genome-scale CRISPR screening identifies tRCC-selective vulnerabilities linked to this metabolic state, including EGLN1, which hydroxylates HIF-1 and targets it for proteolysis. Inhibition of EGLN1 compromises tRCC cell growth by stabilizing HIF-1a and promoting metabolic reprogramming away from OXPHOS, thus representing a vulnerability to OXPHOS-dependent tRCC cells. Our study defines a distinctive tRCC-essential metabolic program driven by TFE3 fusions and nominates EGLN1 inhibition as a therapeutic strategy to counteract fusion-induced metabolic rewiring.

cancer biology↗

bioRxiv2024

Two TAL effectors of Xanthomonas citri pv. malvacearum target GhSWEET15 as the susceptibility genes for bacterial blight of cotton

Bacterial Blight of Cotton (BBC) caused by Xanthomonas citri pv. malvacearum (Xcm) is an important and destructive disease affecting cotton plants. Transcription activator-like effectors (TALEs) released by the pathogen regulate cotton resistance to the susceptibility. In this study, we sequenced the whole genome of Xcm Xss-V2-18 and identified eight tal genes; seven on the plasmids and one on the chromosome. Deletion and complementation experiments of Xss-V2-18 tal genes demonstrated that Tal1b is required for full virulence on cotton. Transcriptome profiling coupled with TALE-binding element prediction revealed that Tal1b targets GhSWEET15A04/D04 and GhSWEET15D02 simultaneously. Expression analysis confirmed the independent inducibility of GhSWEET15A04/D04 and GhSWEET15D02 by Tal1b, whereas GhSWEET15A04/D04 is additionally targeted by Tal1. Moreover, GUS ({beta}-glucuronidase) and Xa10-mediated HR (hypersensitive response) assays indicated that the EBEs are required for the direct and specific activation of the candidate targets by Tal1 and Ta1b. These findings may advance our understanding of the dynamics between TALEs and EBEs, and decipher a simple and effective DNA-binding mechanism that could lead to the development of more efficient methods for gene editing and transgenic research.

pathology↗

bioRxiv2024

Effects of healthy aging on tongue-jaw kinematics during feeding behavior in rhesus macaques

Several age-related oral health problems have been associated with neurodegenerative diseases such as Alzheimers Disease (AD), yet how oromotor dysfunction in healthy aging differ from those found in pathological aging is still unknown. This is partly because changes in the cortical and biomechanical ("neuromechanical") control of oromotor behavior in healthy aging are poorly understood. To this end, we investigated the natural feeding behavior of young and aged rhesus macaques (Macaca mulatta) to understand the age-related differences in tongue and jaw kinematics. We tracked tongue and jaw movements in 3D using high-resolution biplanar videoradiography and X-ray Reconstruction of Moving Morphology (XROMM). Older subjects exhibited a reduced stereotypy in tongue movements during chews and a greater lag in tongue movements relative to jaw movements compared to younger subjects. Overall, our findings reveal age-related changes in tongue and jaw kinematics, which may indicate impaired tongue-jaw coordination. Our results have important implications for the discovery of potential neuromechanical biomarkers for early diagnosis of AD.

systems biology↗

bioRxiv2024

VASCilia (Vision Analysis StereoCilia): A Napari Plugin for Deep Learning-Based 3D Analysis of Cochlear Hair Cell Stereocilia Bundles

Cochlear hair cells are essential for hearing, and their stereocilia bundles are critical for mechanotransduction. However, analyzing the 3D morphology of these bundles can be challenging due to their complex organization and the presence of other cellular structures in the tissue. To address this, we developed VASCilia (Vision Analysis StereoCilia), a Napari plugin suite that automates the analysis of 3D confocal microscopy datasets of phalloidin-stained cochlear hair cell bundles. VASCilia includes five deep learning-based models that streamline the analysis process, including: (1) Z-Focus Tracker (ZFT) for selecting relevant slices in a 3D image stack; (2) PCPAlignNet (Planar Cell Polarity Alignment Network) for automated orientation of image stacks; (3) a segmentation model for identifying and delineating stereocilia bundles; (4) a tonotopic Position Prediction tool; and (5) a classification tool for identifying hair cell subtypes. In addition, VASCilia provides automated computational tools and measurement capabilities. Using VASCilia, we demonstrate its utility on challenging datasets, including neonatal wildtype and Eps8 KO 5-day old mice), revealing that total actin content does not necessarily scale with bundle height. We further showcase its power by quantifying complex bundle disorganization in Cdh23-/- cochleae via texture analysis, which revealed systematically more heterogeneous and less regular bundles than in controls. These novel biological findings demonstrate the power of VASCilia in facilitating detailed quantitative analysis of stereocilia. VASCilia also provides a user-friendly interface that allows researchers to easily navigate and use the tool, with the added capability to reload all their analyses for review or sharing purposes. We believe that VASCilia will be a valuable resource for researchers studying cochlear hair cell development and function, addressing a longstanding need in the hair cell research community for specialized deep learning-based tools capable of high-throughput image quantitation. We have released our code along with a manually annotated dataset that includes approximately 55 3D stacks featuring instance segmentation (https://github.com/ucsdmanorlab/Napari-VASCilia). This dataset comprises a total of 502 inner and 1,703 outer hair cell bundles annotated in 3D. As the first open-source dataset of its kind, we aim to establish a foundational resource for constructing a comprehensive atlas of cochlea hair cell images. Ultimately, this initiative will support the development of foundational models adaptable to various species, markers, and imaging scales to accelerate advances within the hearing research community.

bioinformatics↗

bioRxiv2024

TDC-2: Multimodal Foundation for Therapeutic Science

Drug discovery AI datasets and benchmarks have not traditionally included single-cell analysis biomarkers. While benchmarking efforts in single-cell analysis have recently released collections of single-cell tasks, they have yet to comprehensively release datasets, models, and benchmarks that integrate a broad range of therapeutic discovery tasks with cell-type-specific biomarkers. Therapeutics Commons (TDC-2) presents datasets, tools, models, and benchmarks integrating cell-type-specific contextual features with ML tasks across therapeutics. We present four tasks for contextual learning at single-cell resolution: drug-target nomination, genetic perturbation response prediction, chemical perturbation response prediction, and protein-peptide interaction prediction. We introduce datasets, models, and benchmarks for these four tasks. Finally, we detail the advancements and challenges in machine learning and biology that drove the implementation of TDC-2 and how they are reflected in its architecture, datasets and benchmarks, and foundation model tooling.

bioinformatics↗

bioRxiv2024

CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments

Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, Large Language Models (LLMs) often lack domain-specific knowledge and struggle to accurately solve biological design problems. We present CRISPR-GPT, an LLM agent system to automate and enhance CRISPR-based gene-editing design and data analysis. CRISPR-GPT leverages the reasoning capabilities of LLMs for complex task decomposition, decision-making, and interactive human-artificial intelligence (AI) collaboration. This system incorporates domain expertise, retrieval techniques, external tools, and a specialized LLM fine-tuned with open-forum discussions among scientists. CRISPR-GPT assists users in selecting CRISPR systems, experiment planning, designing gRNAs, choosing delivery methods, drafting protocols, designing assays, and analyzing data. We showcase the potential of CRISPR-GPT by knocking-out four genes with CRISPR-Cas12a in a human lung adenocarcinoma cell line and epigenetically activating two genes using CRISPR-dCas9 in human melanoma cell line. CRISPR-GPT enables fully AI-guided gene-editing experiment design and analysis across different modalities, validating its effectiveness as an AI co-pilot in genome engineering.

bioinformatics↗

bioRxiv2024

Flow zoometry of Drosophila

Drosophila serves as a highly valuable model organism across numerous fields including genetics, immunology, neuroscience, cancer biology, and developmental biology. Central to Drosophila-based biological research is the ability to perform comprehensive genetic or chemical screens. However, this research is often limited by its dependence on laborious manual handling and analysis, making it prone to human error and difficult to discern statistically significant or rare events amid the noise of individual variations resulting from genetic and environmental factors. In this article we present flow zoometry, a whole-animal equivalent of flow cytometry for large-scale, individual-level, high-content screening of Drosophila. Our flow zoometer automatically clears the tissues of Drosophila melanogaster, captures three-dimensional (3D) multi-color fluorescence tomograms of single flies with single-cell volumetric resolution at an unprecedented throughput of over 1,000 animals within 48 hours (24 hr for clearing; 24 hr for imaging), and performs AI-enhanced data-driven analysis - a task that would traditionally take months or years with manual techniques. To demonstrate its broad applications, we employed the flow zoometer in various laborious screening assays, including those in toxicology, genotyping, and tumor screening. Flow zoometry represents a pivotal evolution in high-throughput screening technology: previously from molecules to cells, now from cells to whole animals. This advancement serves as a foundational platform for "statistical spatial biology", to improve empirical precision and enable serendipitous discoveries across various fields of biology.

bioengineering↗

bioRxiv2024

Challenges in quantifying functional redundancy and selection in microbial communities

Microbiomes can exhibit large variations in species abundances but high reproducibility in abundances of functional units, an observation often considered evidence for functional redundancy. Based on such reduction in functional variability, selection is hypothesized to act on functional units in these ecosystems. However, the link between functional redundancy and selection remains unclear. Here, we show that reduction in functional variability does not always imply selection on functional profiles. We propose empirical null models to account for the confounding effects of statistical averaging and bias toward environment-independent beneficial functions. We apply our models to existing data sets, and find that the abundances of metabolic groups within microbial communities from bromeliad foliage do not exhibit any evidence of the previously hypothesized functional selection. By contrast, communities of soil bacteria or human gut commensals grown in vitro are selected for metabolic capabilities. By separating the effects of averaging and functional bias on functional variability, we find that the appearance of functional selection in gut microbiome samples from the Human Microbiome Project is artifactual, and that there is no evidence of selection for any molecular function represented by KEGG orthology. These concepts articulate a basic framework for quantifying functional redundancy and selection, advancing our understanding of the mapping between microbiome taxonomy and function.

ecology↗