Identification of bacteria-derived HLA-bound peptides in melanoma.

A variety of species of bacteria are known to colonize human tumours1-11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12-14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen class I and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17 melanoma metastases (derived from 9 patients) revealed 248 and 35 unique HLA-I and HLA-II peptides, respectively, that were derived from 41 species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy.

Priming avocado with sodium hydrosulfide prior to frost conditions induces the expression of genes involved in protection and stress responses.

Priming plants with chemical agents has been extensively investigated as a means for improving their tolerance to many biotic and abiotic stresses. Earlier, we showed that priming young avocado (Persea americana Mill cv. 'Hass') trees with sodium hydrosulfide (NaHS), a donor of hydrogen sulfide, improves the response of photosynthesis to simulated frost (cold followed by high light) conditions. In the current study, we performed a transcriptome analysis to gain insight into the molecular response of avocado 'Hass' leaves to frost, with or without NaHS priming. The analysis revealed 2144 (down-regulated) and 2064 (up-regulated) differentially expressed genes (DEGs) common to both non-primed and primed trees. Non-primed trees had 697 (down) and 559 (up) unique DEGs, while primed trees exhibited 1395 (down) and 1385 (up) unique DEGs. We focus on changes in the expression patterns of genes encoding proteins involved in photosynthesis, carbon cycle, protective functions, biosynthesis of isoprenoids and abscisic acid (ABA), as well as ABA-regulated genes. Notably, the differential expression results depict the enhanced response of primed trees to the frost and highlight gene expression changes unique to primed trees. Amongst these are up-regulated genes encoding pathogenesis-related proteins, heat shock proteins, enzymes for ABA metabolism, and ABA-induced transcription factors. Extending the priming experiments to field conditions, which showed a benefit to the physiology of trees following chilling, suggests that it can be a possible means to improve trees' response to cold stress under natural winter conditions.

Fundamental helical geometry consolidates the plant photosynthetic membrane.

Plant photosynthetic (thylakoid) membranes are organized into complex networks that are differentiated into 2 distinct morphological and functional domains called grana and stroma lamellae. How the 2 domains join to form a continuous lamellar system has been the subject of numerous studies since the mid-1950s. Using different electron tomography techniques, we found that the grana and stroma lamellae are connected by an array of pitch-balanced right- and left-handed helical membrane surfaces of different radii and pitch. Consistent with theoretical predictions, this arrangement is shown to minimize the surface and bending energies of the membranes. Related configurations were proposed to be present in the rough endoplasmic reticulum and in dense nuclear matter phases theorized to exist in neutron star crusts, where the right- and left-handed helical elements differ only in their handedness. Pitch-balanced helical elements of alternating handedness may thus constitute a fundamental geometry for the efficient packing of connected layers or sheets.

Thylakoid membrane stacking controls electron transport mode during the dark-to-light transition by adjusting the distances between PSI and PSII.

The balance between linear electron transport (LET) and cyclic electron transport (CET) plays an essential role in plant adaptation and protection against photo-induced damage. This balance is largely maintained by phosphorylation-driven alterations in the PSII-LHCII assembly and thylakoid membrane stacking. During the dark-to-light transition, plants shift this balance from CET, which prevails to prevent overreduction of the electron transport chain and consequent photo-induced damage, towards LET, which enables efficient CO2 assimilation and biomass production. Using freeze-fracture cryo-scanning electron microscopy and transmission electron microscopy of Arabidopsis leaves, we reveal unique membrane regions possessing characteristics of both stacked and unstacked regions of the thylakoid network that form during this transition. A notable consequence of the morphological attributes of these regions, which we refer to as 'stacked thylakoid doublets', is an overall increase in the proximity and connectivity of the two photosystems (PSI and PSII) that drive LET. This, in turn, reduces diffusion distances and barriers for the mobile carriers that transfer electrons between the two PSs, thereby maximizing LET and optimizing the plant's ability to utilize light energy. The mechanics described here for the shift between CET and LET during the dark-to-light transition are probably also used during chromatic adaptation mediated by state transitions.

CellSighter: a neural network to classify cells in highly multiplexed images.

Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we present CellSighter, a deep-learning based pipeline to accelerate cell classification in multiplexed images. Given a small training set of expert-labeled images, CellSighter outputs the label probabilities for all cells in new images. CellSighter achieves over 80% accuracy for major cell types across imaging platforms, which approaches inter-observer concordance. Ablation studies and simulations show that CellSighter is able to generalize its training data and learn features of protein expression levels, as well as spatial features such as subcellular expression patterns. CellSighter's design reduces overfitting, and it can be trained with only thousands or even hundreds of labeled examples. CellSighter also outputs a prediction confidence, allowing downstream experts control over the results. Altogether, CellSighter drastically reduces hands-on time for cell classification in multiplexed images, while improving accuracy and consistency across datasets.

The Gut Microbiome of Adults With Type 1 Diabetes and Its Association With the Host Glycemic Control.

OBJECTIVE: Previous studies have demonstrated an association between gut microbiota composition and type 1 diabetes (T1D) pathogenesis. However, little is known about the composition and function of the gut microbiome in adults with longstanding T1D or its association with host glycemic control. RESEARCH DESIGN AND METHODS: We performed a metagenomic analysis of the gut microbiome obtained from fecal samples of 74 adults with T1D, 14.6 ± 9.6 years following diagnosis, and compared their microbial composition and function to 296 age-matched healthy control subjects (1:4 ratio). We further analyzed the association between microbial taxa and indices of glycemic control derived from continuous glucose monitoring measurements and blood tests and constructed a prediction model that solely takes microbiome features as input to evaluate the discriminative power of microbial composition for distinguishing individuals with T1D from control subjects. RESULTS: Adults with T1D had a distinct microbial signature that separated them from control subjects when using prediction algorithms on held-out subjects (area under the receiver operating characteristic curve = 0.89 ± 0.03). Linear discriminant analysis showed several bacterial species with significantly higher scores in T1D, including Prevotella copri and Eubacterium siraeum, and species with higher scores in control subjects, including Firmicutes bacterium and Faecalibacterium prausnitzii (P < 0.05, false discovery rate corrected for all). On the functional level, several metabolic pathways were significantly lower in adults with T1D. Several bacterial taxa and metabolic pathways were associated with the host's glycemic control. CONCLUSIONS: We identified a distinct gut microbial signature in adults with longstanding T1D and associations between microbial taxa, metabolic pathways, and glycemic control indices. Additional mechanistic studies are needed to identify the role of these bacteria for potential therapeutic strategies.

Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease.

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD.

Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy.

Information theoretic approaches are ubiquitous and effective in a wide variety of bioinformatics applications. In comparative genomics, alignment-free methods, based on short DNA words, or k-mers, are particularly powerful. We evaluated the utility of varying k-mer lengths for genome comparisons by analyzing their sequence space coverage of 5805 genomes in the KEGG GENOME database. In subsequent analyses on four k-mer lengths spanning the relevant range (11, 21, 31, 41), hierarchical clustering of 1634 genus-level representative genomes using pairwise 21- and 31-mer Jaccard similarities best recapitulated a phylogenetic/taxonomic tree of life with clear boundaries for superkingdom domains and high subtree similarity for named taxons at lower levels (family through phylum). By analyzing ~14.2M prokaryotic genome comparisons by their lowest-common-ancestor taxon levels, we detected many potential misclassification errors in a curated database, further demonstrating the need for wide-scale adoption of quantitative taxonomic classifications based on whole-genome similarity.

The human tumor microbiome is composed of tumor type-specific intracellular bacteria.

Bacteria were first detected in human tumors more than 100 years ago, but the characterization of the tumor microbiome has remained challenging because of its low biomass. We undertook a comprehensive analysis of the tumor microbiome, studying 1526 tumors and their adjacent normal tissues across seven cancer types, including breast, lung, ovary, pancreas, melanoma, bone, and brain tumors. We found that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome. The intratumor bacteria are mostly intracellular and are present in both cancer and immune cells. We also noted correlations between intratumor bacteria or their predicted functions with tumor types and subtypes, patients' smoking status, and the response to immunotherapy.