Enhancing radiotherapy response via intratumoral injection of the TLR9 agonist CpG to stimulate CD8 T cells in an autochthonous mouse model of sarcoma.

Radiation therapy is frequently used to treat cancers including soft tissue sarcomas. Prior studies established that the toll-like receptor 9 (TLR9) agonist cytosine-phosphate-guanine oligodeoxynucleotide (CpG) enhances the response to radiation therapy (RT) in transplanted tumors, but the mechanism(s) remain unclear. Here, we used CRISPR/Cas9 and the chemical carcinogen 3-methylcholanthrene (MCA) to generate autochthonous soft tissue sarcomas with high tumor mutation burden. Treatment with a single fraction of 20 Gy RT and two doses of CpG significantly enhanced tumor response, which was abrogated by genetic or immunodepletion of CD8+ T cells. To characterize the immune response to RT + CpG, we performed bulk RNA-seq, single-cell RNA-seq, and mass cytometry. Sarcomas treated with 20 Gy and CpG demonstrated increased CD8 T cells expressing markers associated with activation and proliferation, such as Granzyme B, Ki-67, and interferon-γ. CpG + RT also upregulated antigen presentation pathways on myeloid cells. Furthermore, in sarcomas treated with CpG + RT, TCR clonality analysis suggests an increase in clonal T-cell dominance. Collectively, these findings demonstrate that RT + CpG significantly delays tumor growth in a CD8 T cell-dependent manner. These results provide a strong rationale for clinical trials evaluating CpG or other TLR9 agonists with RT in patients with soft tissue sarcoma.

Universal gut microbial relationships in the gut microbiome of wild baboons.

Ecological relationships between bacteria mediate the services that gut microbiomes provide to their hosts. Knowing the overall direction and strength of these relationships is essential to learn how ecology scales up to affect microbiome assembly, dynamics, and host health. However, whether bacterial relationships are generalizable across hosts or personalized to individual hosts is debated. Here, we apply a robust, multinomial logistic-normal modeling framework to extensive time series data (5534 samples from 56 baboon hosts over 13 years) to infer thousands of correlations in bacterial abundance in individual baboons and test the degree to which bacterial abundance correlations are 'universal'. We also compare these patterns to two human data sets. We find that, most bacterial correlations are weak, negative, and universal across hosts, such that shared correlation patterns dominate over host-specific correlations by almost twofold. Further, taxon pairs that had inconsistent correlation signs (either positive or negative) in different hosts always had weak correlations within hosts. From the host perspective, host pairs with the most similar bacterial correlation patterns also had similar microbiome taxonomic compositions and tended to be genetic relatives. Compared to humans, universality in baboons was similar to that in human infants, and stronger than one data set from human adults. Bacterial families that showed universal correlations in human infants were often universal in baboons. Together, our work contributes new tools for analyzing the universality of bacterial associations across hosts, with implications for microbiome personalization, community assembly, and stability, and for designing microbiome interventions to improve host health.

Communities of bacteria living in the guts of humans and other animals perform essential services for their hosts such as digesting food, degrading toxins, or fighting viruses and other bacteria that cause disease. These services emerge from so-called 'ecological' relationships between different species of bacteria. One species, for example, may break down a molecule in human food into another compound that is, in turn, digested by another species into a small molecule that the human gut can absorb and use. The bacteria involved in such a process may become more or less common together in their host. In other situations, some bacteria may have opposing roles to each other, meaning that if one species becomes more abundant it may reduce the level of the other. However, it is not known if relationships between different bacteria are consistent across hosts (i.e., universal) or unique to each host (personalized). In other words, if a pair of bacteria increase and decrease in abundance together in one host, do they do the same in other hosts? Microbes often swap genes with each other to gain new traits; as each host harbors a distinctive set of gut microbes, it may be possible for microbial relationships to change depending on the bacterial species present in a specific environment. To investigate, Roche et al. studied the bacteria in thousands of samples of feces collected from 56 baboons over a 13-year period. These samples came from a long-term research project in Amboseli, Kenya which has been studying a population of wild baboons continuously since 1971. Roche et al. measured the abundance of hundreds of gut bacteria in the feces to understand the relationships between pairs. This revealed that connections between species were largely universal rather than personalized to each baboon. Furthermore, the pairs of bacteria with the strongest positive or negative associations had the most consistent relationships across the baboons. Microbial relationships that have strong effects on the microbiome's composition might therefore be especially universal. Further analyses measuring gut bacteria in human babies also found that relationships between pairs of bacteria were largely universal. Hence, individual species of bacteria may fill similar ecological roles in each host across humans and other primates, and perhaps also in other mammals. These findings suggest that it may be possible to leverage the ecological relationships between bacteria to develop universal therapies for human diseases associated with gut bacteria, such as inflammatory bowel disease or Clostridium difficile infection.

Identifying Risk Factors for Blindness From Glaucoma at First Presentation to a Tertiary Clinic.

PURPOSE: Glaucoma is the leading cause of irreversible blindness, a crippling disability resulting in higher risks of chronic health conditions. To better understand disparities in blindness risk, we identified risk factors of blindness on first presentation to a glaucoma clinic using a large clinical database. DESIGN: Retrospective cross-sectional study. METHODS: We used electronic health records of glaucoma patients from the Duke Ophthalmic Registry. International Classification of Diseases codes were used to identify glaucoma and exclude concurrent diseases. Blindness classification was based on the definition of legal blindness. Risk factors included gender, race, marital status, age, intraocular pressure, diabetes history, income level, and education. Odds ratios (ORs) and 95% CIs were calculated for risk factors using univariable and multivariable logistic regression. RESULTS: Our cohort consisted of 3753 patients, with 192 (5%) blind on first presentation. In univariable models, African American / Black race (OR 2.48, 95% CI 1.83-3.36), single marital status (1.74, 95% CI 1.25-2.44), prior diabetes diagnosis (2.23, 95% CI 1.52-3.27), and higher intraocular pressure (1.29 per 1 SD higher, 95% CI 1.13-1.46) were associated with increased risk of presenting blind, whereas higher annual income (0.75, 95% CI 0.65-0.86) and education (0.77, 95% CI 0.69-0.85) were associated with lower risk. These associations remained significant and in the same direction in a multivariable model apart from income, which became insignificant. CONCLUSIONS: Using a large real-world clinical database, we identified risk factors associated with presentation with blindness among glaucoma patients. Our results highlight disparities in health care outcomes and indicate the importance of targeted education to reduce disparities in blindness.

The accuracy of absolute differential abundance analysis from relative count data.

Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. For example, in the differential abundance setting, compositional effects in relative abundance data may give rise to spurious differences (false positives) when considered from the absolute perspective. In practice however, relative abundances are often transformed by renormalization strategies intended to compensate for these effects and the scope of the practical problem remains unclear. We used simulated data to explore the consistency of differential abundance calling on renormalized relative abundances versus absolute abundances and find that, while overall consistency is high, with a median sensitivity (true positive rates) of 0.91 and specificity (1-false positive rates) of 0.89, consistency can be much lower where there is widespread change in the abundance of features across conditions. We confirm these findings on a large number of real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, where data sets with the greatest change between experimental conditions are also those with the highest false positive rates. Finally, we evaluate the predictive utility of summary features of relative abundance data themselves. Estimates of sparsity and the prevalence of feature-level change in relative abundance data give reasonable predictions of discrepancy in differential abundance calling in simulated data and can provide useful bounds for worst-case outcomes in real data.

Antibiotics and fecal transfaunation differentially affect microbiota recovery, associations, and antibiotic resistance in lemur guts.

BACKGROUND: Antibiotics alter the diversity, structure, and dynamics of host-associated microbial consortia, including via development of antibiotic resistance; however, patterns of recovery from microbial imbalances and methods to mitigate associated negative effects remain poorly understood, particularly outside of human-clinical and model-rodent studies that focus on outcome over process. To improve conceptual understanding of host-microbe symbiosis in more naturalistic contexts, we applied an ecological framework to a non-traditional, strepsirrhine primate model via long-term, multi-faceted study of microbial community structure before, during, and following two experimental manipulations. Specifically, we administered a broad-spectrum antibiotic, either alone or with subsequent fecal transfaunation, to healthy, male ring-tailed lemurs (Lemur catta), then used 16S rRNA and shotgun metagenomic sequencing to longitudinally track the diversity, composition, associations, and resistomes of their gut microbiota both within and across baseline, treatment, and recovery phases. RESULTS: Antibiotic treatment resulted in a drastic decline in microbial diversity and a dramatic alteration in community composition. Whereas microbial diversity recovered rapidly regardless of experimental group, patterns of microbial community composition reflected long-term instability following treatment with antibiotics alone, a pattern that was attenuated by fecal transfaunation. Covariation analysis revealed that certain taxa dominated bacterial associations, representing potential keystone species in lemur gut microbiota. Antibiotic resistance genes, which were universally present, including in lemurs that had never been administered antibiotics, varied across individuals and treatment groups. CONCLUSIONS: Long-term, integrated study post antibiotic-induced microbial imbalance revealed differential, metric-dependent evidence of recovery, with beneficial effects of fecal transfaunation on recovering community composition, and potentially negative consequences to lemur resistomes. Beyond providing new perspectives on the dynamics that govern host-associated communities, particularly in the Anthropocene era, our holistic study in an endangered species is a first step in addressing the recent, interdisciplinary calls for greater integration of microbiome science into animal care and conservation.

The Pediatric Obesity Microbiome and Metabolism Study (POMMS): Methods, Baseline Data, and Early Insights.

OBJECTIVE: The purpose of this study was to establish a biorepository of clinical, metabolomic, and microbiome samples from adolescents with obesity as they undergo lifestyle modification. METHODS: A total of 223 adolescents aged 10 to 18 years with BMI ≥95th percentile were enrolled, along with 71 healthy weight participants. Clinical data, fasting serum, and fecal samples were collected at repeated intervals over 6 months. Herein, the study design, data collection methods, and interim analysis-including targeted serum metabolite measurements and fecal 16S ribosomal RNA gene amplicon sequencing among adolescents with obesity (n = 27) and healthy weight controls (n = 27)-are presented. RESULTS: Adolescents with obesity have higher serum alanine aminotransferase, C-reactive protein, and glycated hemoglobin, and they have lower high-density lipoprotein cholesterol when compared with healthy weight controls. Metabolomics revealed differences in branched-chain amino acid-related metabolites. Also observed was a differential abundance of specific microbial taxa and lower species diversity among adolescents with obesity when compared with the healthy weight group. CONCLUSIONS: The Pediatric Metabolism and Microbiome Study (POMMS) biorepository is available as a shared resource. Early findings suggest evidence of a metabolic signature of obesity unique to adolescents, along with confirmation of previously reported findings that describe metabolic and microbiome markers of obesity.

Naught all zeros in sequence count data are the same.

Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply different zero-handling models to gene-expression and microbiome datasets and show models can disagree substantially in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as "zero-inflation" was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.

An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum.

The blood stage of the infection of the malaria parasite Plasmodium falciparum exhibits a 48-hour developmental cycle that culminates in the synchronous release of parasites from red blood cells, which triggers 48-hour fever cycles in the host. This cycle could be driven extrinsically by host circadian processes or by a parasite-intrinsic oscillator. To distinguish between these hypotheses, we examine the P. falciparum cycle in an in vitro culture system and show that the parasite has molecular signatures associated with circadian and cell cycle oscillators. Each of the four strains examined has a different period, which indicates strain-intrinsic period control. Finally, we demonstrate that parasites have low cell-to-cell variance in cycle period, on par with a circadian oscillator. We conclude that an intrinsic oscillator maintains Plasmodium's rhythmic life cycle.

Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes.

We applied two state-of-the-art, knowledge independent data-mining methods - Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) - to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tumor types can sort the tumors into groups enriched for relevant annotations including tumor type, gender, tumor stage, and ethnicity. DQC feature selection analysis discovered 48 core biomarker transcripts that clustered tumors by tumor type. When these transcripts were removed, the geometry of tumor relationships changed, but it was still possible to classify the tumors using the RNA expression profiles of the remaining transcripts. We continued to remove the top biomarkers for several iterations and performed cluster analysis. Even though the most informative transcripts were removed from the cluster analysis, the sorting ability of remaining transcripts remained strong after each iteration. Further, in some iterations we detected a repeating pattern of biological function that wasn't detectable with the core biomarker transcripts present. This suggests the existence of a "background classification" potential in which the pattern of gene expression after continued removal of "biomarker" transcripts could still classify tumors in agreement with the tumor type.

Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study.

A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.

Cancer cell redirection biomarker discovery using a mutual information approach.

Introducing tumor-derived cells into normal mammary stem cell niches at a sufficiently high ratio of normal to tumorous cells causes those tumor cells to undergo a change to normal mammary phenotype and yield normal mammary progeny. This phenomenon has been termed cancer cell redirection. We have developed an in vitro model that mimics in vivo redirection of cancer cells by the normal mammary microenvironment. Using the RNA profiling data from this cellular model, we examined high-level characteristics of the normal, redirected, and tumor transcriptomes and found the global expression profiles clearly distinguish the three expression states. To identify potential redirection biomarkers that cause the redirected state to shift toward the normal expression pattern, we used mutual information relationships between normal, redirected, and tumor cell groups. Mutual information relationship analysis reduced a dataset of over 35,000 gene expression measurements spread over 13,000 curated gene sets to a set of 20 significant molecular signatures totaling 906 unique loci. Several of these molecular signatures are hallmark drivers of the tumor state. Using differential expression as a guide, we further refined the gene set to 120 core redirection biomarker genes. The expression levels of these core biomarkers are sufficient to make the normal and redirected gene expression states indistinguishable from each other but radically different from the tumor state.