Biology subjects

Ho, A.

Publications and source records attributed to Ho, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Sleep spindles stabilize human thalamocortical networks, inhibiting pathological disruptions

The thalamus coordinates sleep-dependent brain function including memory consolidation, sensory gating, and network stability through spindle oscillations. Thalamocortical circuits are also affected in many neurological and neuropsychiatric disorders. Yet how human thalamocortical circuits respond to pathological disruptions remain poorly understood. Here, we leveraged interictal epileptiform discharges (IEDs) as spontaneous perturbations to probe spindle-generating circuits and thalamocortical dynamics in vivo. Using multi-night intracranial recordings spanning multiple thalamic nuclei and cortical regions in 55 individuals with epilepsy, we investigated interactions between sleep spindles and epileptic activity across timescales from milliseconds to days. Sleep spindles were associated with IED suppression, whereas IEDs increased subsequent spindle probability, revealing bidirectional interactions between physiological and pathological activity. Nights with lower IEDs corresponded to increased spindle occurrence, longer duration, and faster frequency, reflecting a more stable thalamocortical state. These findings propose sleep spindles as potential regulators of thalamocortical networks that actively respond to and regulate pathological activity.

neuroscience↗

bioRxiv2026

LifeSciBench: Evaluating Language Models on Realistic, Expert-Level Tasks in the Life Sciences

We introduce LifeSciBench, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work. The majority of existing life sciences benchmarks have a narrow scope or are purely knowledge-based, and therefore fail to capture the complexity of real-world research, which often involves ambiguities and requires the accurate execution of multiple dependent judgment calls. Additionally, almost all existing benchmarks span at best a small collection of subdomains within the life sciences; there is at present no existing life sciences benchmark with both the requisite breadth and depth required to convincingly measure proficiency in real-world professional research settings. LifeSciBench addresses this gap by spanning seven representative scientific workflows and seven life science domains, with each constituent task paired with a human expert-written rubric. Across five frontier and domain-specialized models, GPT-Rosalind performs best, with a task-weighted mean normalized rubric score of 0.576 and a task-weighted response pass rate of 36.1% (response-level values are first averaged within each task, and the resulting task-level values are then averaged with equal weight). LifeSciBench remains unsaturated, with 171 tasks (22.8%) having no observed passing response from any evaluated model and 261 tasks (34.8%) having a best-model pass rate below 20%. LifeSciBench therefore serves as a high-resolution evaluation of practical scientific reasoning and operational decision-making in the life sciences.

bioinformatics↗

bioRxiv2026

Scientific computing in the age of agentic AI: an exploratory field report

Scientific computing has become a central component of modern scientific discovery. Yet many computational tools are developed by small, specialized teams under incentives that encourage the release of rapidly prototyped tooling without commensurate attention to engineering concerns, including performance and maintainability. These gaps are particularly visible in the life sciences, where the advent of high-throughput sequencing and molecular profiling has made the production and processing of datasets routine at scales that strain reliability and cost. Recently, LLM-based agents have become increasingly capable, with publicly available systems possessing both significant domain knowledge in many scientific fields and the ability to autonomously operate over complex and specialized codebases in pursuit of well-defined goals. Together, these developments create a practical opportunity for scientific computing. Many of the persistent weaknesses of the scientific computing ecosystem stem from technical debt and a shortage of sustained engineering labor and expertise. Here, we examine coding agents as a potential way to address these weaknesses: we present an exploratory field report of eight early case studies in the application of LLM agents to scientific computing across a range of project scopes, from lightweight maintenance tasks to full performance-oriented rewrites of scientific libraries, with a focus on the life sciences. Each of these case studies is accompanied by reflections from the individual or group responsible for the work, including lessons from the process. Overall, we find that the use of coding agents in scientific computing holds great promise for accelerating scientific research and increasing the reliability of critical systems, but that outstanding concerns remain, including responsibility and ownership for such projects, and we suggest collaboration and stewardship with existing maintainers when feasible.

bioinformatics↗

bioRxiv2026

Bacteriophage treatment of Pseudomonas aeruginosa PA14 infection does not alter the native microbiome of Caenorhabditis elegans

Bacteriophage therapeutics are alternatives to antibiotics for treating multi-drug-resistant (MDR) bacterial infections. Bacteriophages kill host bacteria species with targeted precision, unlike the broad-spectrum nature of antibiotics. While the precision and the natural occurrence of phages in the environment have drawn attention to phages as new "drugs" in the time of accelerated antibiotic resistance, it remains poorly understood how phage treatment impacts the community of organisms composing the microbiome. Before phage therapies can be more widespread in practice, this understanding is required. Caenorhabditis elegans lends itself as an excellent model organism to address this question, as worm populations are raised under sterile conditions and the gut microbiome can be populated experimentally by the worms natural bacterivore diet. Here we have established a C. elegans infection and phage treatment model using fluorescent Pseudomonas aeruginosa PA14 to quantify infection. We isolated and whole genome sequenced PA14-specific lytic phage from wastewater samples near West Point, New York. To characterize the impact of treatment on the microbiome, we populated the C. elegans gut microbiome with 11 strains of the native C. elegans microbiome (CeMbio) and used PacBio sequencing of the full length 16S rRNA gene to characterize the microbiome during PA14 infection in the presence and absence of phage treatment (n = 100 worms per sample, 4 replicates per condition). Taxonomic classification, phylogenetic analysis, and diversity analysis performed using a HiFi 16S bioinformatics pipeline revealed that phage treatment and infection level did not change the overall composition of the C. elegans gut microbiome. ImportanceMultidrug resistant infections are emerging faster than new antibiotic compounds can be synthesized. Bacteriophages are natural viruses of bacteria that seek out and kill only their host bacteria species. Bacteriophage therapeutics are a promising solution for drug resistant infections in patients where no other treatment exists. However, while phage treatment is effective, there is little understanding about what happens within the complex community of the microbiome when a phage treatment is employed. Here we establish a new model utilizing C. elegans to integrate a multidrug resistant infection, phage treatment, and the native microbiome of the C. elegans worm to address how the microbiome changes in response to phage treatment. We find that the microbiome remains unchanged after bacteriophage treatment, highlighting the targeted specificity of phage therapy and suggests fewer side effects for phage therapy than antibiotic treatment for the same infection.

microbiology↗

bioRxiv2026

Bioorthogonal Tuning of Hydrogel Stiffness Promotes Zonal Redifferentiation of Passaged Chondrocytes

Generating bioengineered cartilage that recapitulates the depth-dependent phenotype, structure, and function of native articular cartilage remains a challenge. While cartilage is rich in aggrecan and type II collagen, proper function depends on depth-dependent protein expression. Superficial zone chondrocytes (SZCs) secrete proteoglycan-4 (PRG4) to lubricate the cartilage surface. Deep zone chondrocytes produce type X collagen (COLX) to support compressive loading and load transfer to subchondral bone. We previously demonstrated that passaged full-thickness chondrocytes (FTCs) and zonal chondrocytes can re-express cartilage and zone-specific markers following scaffold-free three-dimensional (3D) culture in redifferentiation media. However, in the absence of an instructive matrix, cells expressed low levels of zone-specific proteins and exhibited limited depth-dependent organization. We hypothesize that synthetic extracellular matrix with zone-specific microenvironmental cues will guide zonal differentiation. To this end, passaged primary bovine chondrocytes were encapsulated in a soft, hyaluronan (HA)-based, cell-adhesive, and protease-degradable hydrogel established via bioorthogonal tetrazine (Tz) ligation with norbornene (Nb). When supplemented with TGF{beta}3, FTCs deposited aggrecan and type II collagen with minimal type I collagen. Application of interfacial tetrazine ligation with trans-cyclooctene (TCO) during cell culture resulted in matrix stiffening, leading to upregulation of COLX expression. Conversely, SZCs cultured in soft hydrogels exhibited the greatest PRG4 expression. Establishment of a trilayered construct with region-specific stiffness via the diffusion-controlled reaction promoted PRG4 and COLX expression in defined zones. Together, these findings demonstrate that tunable HA-based hydrogels can enhance zone-specific chondrocyte phenotypes and promote the formation of zonally organized cartilage.

bioengineering↗

bioRxiv2026

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy

Label-free optical redox imaging utilizes endogenous NAD(P)H and FAD autofluorescence to evaluate metabolism in living specimens. The conventional optical redox ratio collapses these two channels into a single value; however, it does not indicate whether a pixel has sufficient photon support or the cellular context necessary for quantitative aggregation. To address this limitation, we introduce FPhaS, a fixed-calibration phase- autofluorescence framework that integrates quantitative phase imaging (QPI) with simultaneous label-free autofluorescence multi-harmonic microscopy (SLAM), using fluorescence lifetime imaging (FLIM) solely for validation. Because QPI and SLAM are acquired with the same objective, a unified non-biological calibration aligns phase-derived structural data with the autofluorescence frame, yielding a residual error of 0.39 pixels. This calibration is maintained across all biological specimens. This shared geometric reference enables local evaluation of structural and metabolic information, rather than comparing approximately aligned images. FPhaS decomposes the data into cell presence, ratio credibility, and confidence-supported pooling. We validated FPhaS on A549 cells under high and low-photon conditions; the framework is designed to generalize to other cell and tissue types. Confidence-weighted intensity redox estimates were compared with lifetime-derived measurements within mask-locked cellular regions. Concordance improved exclusively when both the denominator photon support and an independent structural criterion were satisfied. The same reference layer generated cell-level descriptors of metabolic content, metabolic-structural organization, and measurement reliability, while also constraining the CombinedWLS reconstruction under diminished fluorescence acquisition. FPhaS redefines label-free metabolic imaging from producing comprehensive ratio maps to identifying regions where optical evidence substantiates quantitative inference.

bioengineering↗

bioRxiv2026

GeneBench: Assessing AI Agents for Multi-Stage Inference Problems in Genomics and Quantitative Biology

We introduce GeneBench, a benchmark for AI agents on realistic multi-stage scientific data analysis in genetics and quantitative biology. Existing biology benchmarks mostly measure knowledge retrieval, execution of routine pipelines, or a single analysis step. Yet they do not capture the broader scope of work that occupies much of computational scientists time: cleaning and normalizing assay, phenotype, or clinical data; exploratory data analysis; statistical model selection and diagnostic iteration; and producing a conclusion that informs a downstream scientific or translational decision. GeneBench addresses this gap with 103 evaluations targeting quantities of direct practical relevance across 10 domains, with a genomics-centered core and adjacent coverage in other omics and quantitative biology settings. Each problem comprises an encapsulated multi-step analysis with staged data, prompts that define a quantity of interest while otherwise providing minimal guidance, and verifiable answers. Solving each problem requires identifying and addressing realistic obstacles such as measurement error, selection bias, confounding, QC failures, and choosing among competing model classes. Through extensive ablation studies, we verify that each problem admits a single defensible answer. Each problem involves multiple dependent decision points; i.e., substantive inferential forks where a plausible wrong choice changes the downstream analysis, such that errors propagate through the inferential chain and into the final graded target. In initial evaluations, the mainline GPT family reaches an eval-level pass rate of 25.0% with GPT-5.5 at the xhigh reasoning setting. In separately reported GPT Pro runs, GPT-5.5 Pro reaches 33.2%, GPT-5.4 Pro reaches 25.6%, and GPT-5.2 Pro reaches 10.8%. Even for the two strongest reported Pro-harness settings, 60.2% and 62.1% of problems, respectively, remain below 20% pass rate over repeated runs. The strongest external baseline, Gemini 3.1 Pro, achieves 11.2%. Models often complete substantial portions of the workflow but exhibit a consistent gap between noticing and acting: they identify local diagnostic signals but fail to propagate the implication to the corresponding analysis decision, and as a result select wrong estimators or persist on initially plausible but incorrect analysis paths. GeneBench therefore measures an emerging capability that remains as yet unreliable. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=46 SRC="FIGDIR/small/720113v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@169b81dorg.highwire.dtl.DTLVardef@1b6a4c0org.highwire.dtl.DTLVardef@1fa6951org.highwire.dtl.DTLVardef@11845da_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Thalamic Interictal Epileptic and Non-Epileptic Events during NREM Sleep in Patients with Focal Epilepsy: a Stereo-EEG Study

BackgroundThalamic recordings are increasingly incorporated into stereo-electroencephalography (SEEG) evaluations of drug-resistant focal epilepsy to guide neuromodulation targeting. Human thalamic electrophysiology, however, is poorly defined, limiting the distinction between pathological and physiological activity. Here, we characterized interictal epileptic and non-epileptic events during non-rapid eye movement (NREM) sleep across multiple thalamic nuclei and examined their relationship to seizure outcomes. MethodsWe analyzed NREM sleep SEEG recordings from 64 patients with drug-resistant focal epilepsy. Electrodes sampled four thalamic nuclei: centromedian (CM), pulvinar (Pu), ventral lateral (VL), and ventral posterolateral (VPL). Patients were classified into three outcome groups: favorable, unfavorable, and surgically non-remediable. Rates of thalamic spikes, high-frequency oscillations (HFOs), spike-fast activity, and sleep spindles were analyzed and compared across nuclei and outcomes. FindingsRecordings of the thalamus revealed both pathological and physiological interictal events. Interictal epileptic events were infrequent. Only [~]0.2% of seizure-onset zone spikes propagated to the thalamus. Thalamic spike-fast activity was indicative of unfavorable surgical outcomes (CM: p = 0.047, d = 0.46) or surgically non-remediable epilepsy (VL: p = 0.002, d = 0.84). In contrast, thalamic sleep spindles were ubiquitous but reduced in surgically non-remediable patients (CM: p = 0.031, d = -0.58; VL: p = 0.005, d = -0.79). Finally, unique thalamic SEEG patterns were identified, including spikes concomitant with spindles, isolated spikes, and physiological fast ripples. InterpretationThis study provides a comprehensive characterization of thalamic interictal events during NREM sleep, enriching our understanding of thalamic pathophysiology and highlighting the value of thalamic recordings in presurgical evaluation.

neuroscience↗

bioRxiv2025

Sleep fragmentation drives local, network-specific epileptic activity in human epilepsy

Sleep has complex links with epileptic activity, yet the causal role of sleep instability in driving and modulating pathological discharges in the human brain remains incompletely understood. Here we directly examine this by characterising the fine-scale temporal coupling between experimentally induced sleep arousals and interictal epileptiform discharges (IEDs), using combined stereo-electroencephalography and polysomnography recordings in patients with epilepsy. Sleep arousals triggered rapid IED increases, with effects gated by anatomical region and sleep stage. Increases were confined to neocortical regions and occurred during both non-rapid eye movement stage 2 (N2) and stage 3 (N3) sleep, with a larger effect observed in N2. IED increases did not differ between the seizure-onset zone and surrounding regions. Despite elevating IED counts, arousals did not alter IED spatial propagation, indicating state-dependent enhancement of local cortical excitability without recruitment of broader epileptic networks. These findings establish a causal role for sleep instability in actively driving pathological activity on fine-grained spatiotemporal scales, and highlight sleep stabilisation as a promising therapeutic strategy to reduce epileptic burden and preserve cortical network function.

neuroscience↗

bioRxiv2025

Variations in cow milk and teat skin microbiota across the lactation cycle with intra-mammary cephalosporin use at dry-off

Cephalosporins and other broad-spectrum antibiotics are frequently administered prophylactically into the udder when dairy cows end their lactation cycle, termed dry-off, to reduce mastitis risk. However, the use of antibiotics on cows that do not have signs of infection may result in the selection of microbes resistant to antibiotics and negatively alter the udder microbiome composition. In this study, the effects of intramammary cephalosporin therapy with either Cephapirin (CB) or Ceftiofur (CH) on milk and teat skin microbiota were examined for three dairies in California. Bacterial composition was measured for cows with low somatic cell counts (SCC,<100,000 cells /mL) and high, subclinical SCC (>200,000 cells/mL). Samples were collected at dry-off (before treatment), seven days later, and 55-75 Days in Milk (DIM) in the next lactation cycle. The milk and skin microbiota were largely separated based on dairy (milk: R2 = 6.22, skin: R2 = 7.56) and day of sampling (milk: R2 = 4.74 and skin: R2 = 3.77). CB or CH use was associated with a small but significant impact on the milk microbiota beta-diversity (Bray-Curtis, p =0.003, R2= 1.4%) but no effect was observed on the skin. At one dairy (Dairy 3), milk from cows receiving CB and CH had a reduction in proportions of Staphylococcaceae at 55-75DIM compared to untreated cows. Overall, antibiotic use did not result in large significant changes to bacterial diversity in milk or on the teat skin, and instead the microbiota at those sites mainly differed between the time and location of sampling. ImportanceThe use of antibiotics in agriculture is under increasing scrutiny due to the rising spread of antimicrobial resistant bacteria. Our study showed that common preventative antibiotic intramammary treatment of cows with cephalosporins at the end their lactation (dry-off) had minimal effects on the milk and teat skin microbiota on asymptomatic cows with high somatic cell counts.

microbiology↗

bioRxiv2025

Few-Shot Classification of Cryo-EM Micrographs Using Triplet Loss Embeddings

Micrograph quality assessment in cryo-electron microscopy (cryo-EM) presents a significant challenge: users must either manually screen thousands of micrographs or expend substantial computational resources processing potentially low-quality data. While few-shot learning has been applied to particle picking and subtomogram classification in cryo-EM, its application to micrograph-level quality assessment remains largely unexplored. We present a framework combining few-shot learning with cryo-EM micrograph classification using triplet loss embeddings. By combining real-space and Fourier-space information in an embedding network trained with triplet loss, we achieve competitive performance across multiple EMPIAR datasets using as few as 1-5 labeled examples per class. Our approach demonstrates improvements over traditional cross-entropy training, which often collapses to predicting only the majority class in the few-shot regime. These results suggest a practical framework for rapid adaptation of automated micrograph screening to new experimental conditions with minimal manual labeling.

biophysics↗

bioRxiv2025

Beyond Circadian: A Yearlong Electroencephalography Study Reveals Hidden Ultralong-term Sleep Cycles

Sleep is essential for brain function and overall health. While circadian rhythms and sleep stages across the night have been well-characterized, long-term variations in sleep remain poorly understood. We used a novel technology, subcutaneous electroencephalography, to collect yearlong sleep recordings from 20 healthy individuals. We investigated intrinsic and extrinsic drivers of sleep variability and identified recurring multi-day cycles of 8-60 days in sleep duration, latency, architecture, and stability. Additionally, sleep was modulated by external factors including season, weather, weekends and holidays. This reveals that while sleep processes are governed by internal dynamics, they remain sensitive to environmental influences. These results uncover a previously unrecognized temporal variation in human sleep and suggest new directions for understanding sleep patterns and their relevance to health and disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/676090v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@177393corg.highwire.dtl.DTLVardef@4ea454org.highwire.dtl.DTLVardef@106bc4corg.highwire.dtl.DTLVardef@1342321_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

mGluR4-Npdc1 complex mediates α-synuclein fibril-induced neurodegeneration

Fibrils of misfolded -synuclein (-syn) accumulate in Parkinsons disease and other synucleinopathies, spreading between cells to template further misfolding and drive neurodegeneration. -syn fibril entry into healthy neurons is recognized as a key step in the disease process but remains ill-defined mechanistically. Here, we comprehensively assessed the membrane proteome for binding of -syn fibrils. Expression cloning identified mGluR4 and Npdc1 as plasma membrane proteins expressed by substantia nigra neurons capable of supporting high affinity -syn fibril binding. Moreover, mGluR4 and Npdc1 cellular signaling functions were titrated by the presence of extracellular fibrillary -syn. While striatal -syn fibril injection led to nigral dopamine neuron loss in wild type mice, deletion of either Grm4 or Npdc1 provided protection of dopamine neurons. We observed mGluR4 and Npdc1 to form a complex that regulates mGluR4 signaling. Cultured neurons lacking both Grm4 and Npdc1 fail to bind -syn fibrils, to accumulate phosphorylated -syn and to lose synapses. Transheterozygous Grm4, Npdc1 mice showed protection from nigral neuron loss after striatal -syn injection, demonstrating genetic interaction between the two binding proteins. On a transgenic -syn A53T background, double Grm4, Npdc1 heterozygosity robustly increased mouse survival, motor function and spinal motoneuron number. Thus, a cell surface mGluR4-Npdc1 complex participates in -syn neurodegeneration.

neuroscience↗

bioRxiv2025

Systematic contextual biases in SegmentNT relevant to all nucleotide transformer models

Recent advances in large language models (LLMs) have extended to genomic applications, yet model robustness relative to context is unclear. Here, we demonstrate two intrinsic biases (input sequence length and nucleotide position) affecting SegmentNT results, a model included with the Nucleotide Transformer that provides nucleotide-level predictions of biological features. We demonstrate that nucleotide position within the input sequence (beginning, middle, or end) alters the nature of SegmentNTs raw prediction probabilities, which can be standardized to improve prediction consistency. While longer input sequence length improves model performance, diminishing returns suggest a surprisingly small input length of [~]3,072 nucleotides might be sufficient for many applications. We further identify a 24-nucleotide periodic oscillation in SegmentNTs prediction probabilities, revealing an intrinsic bias potentially linked to the models training tokenization (6-mers) and architecture. We identify potential approaches to account for these biases and provide generalizable insights for utilizing nucleotide-resolution functional prediction models.

bioinformatics↗

bioRxiv2025

GenVarLoader: An accelerated dataloader for applying deep learning to personalized genomics

Deep learning sequence models trained on personalized genomics can improve variant effect prediction, however, applications of these models are limited by computational requirements for storing and reading large datasets. We address this with GenVarLoader, which stores personalized genomic data in new memory-mapped formats with optimal data locality to achieve [~]1,000x faster throughput and [~]2,000x better compression compared to existing alternatives.

genomics↗

bioRxiv2024

Identification of novel small molecule chaperone activators for neurodegenerative disease treatment

A pathological hallmark of neurodegenerative disease is the accumulation of aberrant protein aggregates which contribute to the cytotoxicity and are therefore a target for new therapy development. One key machinery to manage cellular protein homeostasis is the chaperones proteins, the heat shock proteins (HSPs) which are known to target aberrant proteins including TDP-43, tau and amyloid and rescue neurodegeneration in various disease models. As an attempt to target HSP activation for neurodegeneration therapy, we develop a drug screening assay to search compounds that activate the master regulator of HSPs, the transcription factor heat shock factor 1 (HSF1). As HSF1 is bound by HSP90 which prevents its activation, we develop a NanoBRET assay, which allows us to monitor and quantify the HSF1-HSP90 interaction in living cells to screen for compounds disrupting this interaction and thereby releasing HSF1 for activation. After the assay was optimised and validated for its robustness and reliability, a two thousand compound library was screened which produced 10 hits including a couple of known HSP90 inhibitors. Follow-up functional study showed that one of the hit oxyphenbutazone (OPB) significantly reduces the accumulation of insoluble TDP-43 in a cell model, displaying no signs of stress or toxicity. Further works are needed to characterise the mode-of-action of OPB in HSF1 activation and aberrant protein reduction, and the physiological impact of OPB on TDP-43 needs to be examined in an in vivo model. However, this study demonstrates a viable strategy for new drug discovery in targeting aberrant proteins and identifies potential candidates for translation into neurodegenerative disease treatment.

molecular biology↗

bioRxiv2024

Single RNA molecule resolution spatial imaging of immunotherapy response in triple negative breast tumors harboring tertiary lymphoid structures

Cancer immunotherapy trials have had variable success, prompting a search for biomarkers of response. Tertiary lymphoid structures (TLS) have emerged as prognostic for multiple tumor types. These ectopic immunological bodies resemble secondary lymphoid organs with segregated B and T cell zones, but they are heterogeneous in their organization and cellular composition. These features have consequences in terms of prognostication and disease clearance, so there is interest in what drives TLS heterogeneity and corresponding immunological responses. We applied single RNA molecule resolution imaging to study biopsies from triple negative breast tumors harboring TLS where the biopsies were taken longitudinally, prior to therapy, after pembrolizumab and after pembrolizumab with radiation therapy. We developed a computational framework to identify TLS and tumor beds and to align spatial trajectories between the immune and malignant structures for systematic analyses. We identified two tumor types based on immune infiltration profiles in the tumor bed. Immune "infiltrated" tumors were eliminated after pembrolizumab, while "non-infiltrated" tumors saw gains in effector T cells and myeloid cells after pembrolizumab and were cleared after pembrolizumab with RT. TLS from infiltrated tumors had better separation of B and T cell zones and had higher expression of immunoreactivity gene pathways in most cell types. Further, malignant cell MHC expression was higher in the tumor beds of infiltrated tumors, providing one plausible mechanism for the groupings. In non-infiltrated tumors, classical dendritic cells enter the tumor bed from TLS proximal zones after pembrolizumab and bring transcription of the CXCL9 chemokine, which can recruit T cells and promote T cell effector phenotypes and was higher in infiltrated tumors at baseline.

genomics↗

bioRxiv2024

Selective enrichment of Methylococcaceae versus Methylocystaceae methanotrophs via control of methane feeding scheme

Methanotrophs are crucial in keeping environmental CH4 emissions in check. However, how different groups of methanotrophs contribute to this important role in different environmental settings remain ambiguous. Here, in a simplified laboratory setting of well-mixed batch reactors fed continuous flow of CH4-containing gas, methanotrophic microbiomes were enriched from paddy soils under six different incubation conditions prepared as combinations of two different CH4 mixing ratios (0.5% and 10% v/v) and three supplemented Cu2+ concentrations (0, 2, and 10 M). Monitoring of the temporal community shifts in the reactor microbiomes observed domination of Methylocystis spp. in all three reactors fed 0.5% v/v, as further supported by the analyses of pmoCAB genes in the shotgun metagenomes of the single-point samples from the same reactors. Copper deficiency did not select for mmoXYZ-possessing methanotrophs. Instead, a cluster of mbn genes with an abundance accounting for approximately 5% of Methylocystis population was identified, suggesting a comparative ecological importance of methanobactin in Cu-deficient methanotrophy over soluble methane monooxygenases. These findings highlight the importance of Methylocystis spp. in mitigating emissions from CH4 hotspots, e.g., landfills and rice paddies, and suggest the feasibility of directed enrichment/isolation of Methylocystis spp. for utilization in, for example, methanobactin and polyhydroxybutyrate production. SynopsisThis study reports enrichment of a complex soil microbiota with 0.5% methane resulting in dominance of a specific group of methane-consuming bacteria Methylocystis, highlighting their ecological significance as CH4 sink.

microbiology↗