Expanding the repertoire reveals recurrent, cryptic, and hematopoietic HLA class I minor histocompatibility antigens.

ABSTRACT: Allogeneic stem cell transplantation (alloSCT) is a curative treatment for hematological malignancies. After HLA-matched alloSCT, antitumor immunity is caused by donor T cells recognizing polymorphic peptides, designated minor histocompatibility antigens (MiHAs), that are presented by HLA on malignant patient cells. However, T cells often target MiHAs on healthy nonhematopoietic tissues of patients, thereby inducing side effects known as graft-versus-host disease. Here, we aimed to identify the dominant repertoire of HLA-I-restricted MiHAs to enable strategies to predict, monitor or modulate immune responses after alloSCT. To systematically identify novel MiHAs by genome-wide association screening, T-cell clones were isolated from 39 transplanted patients and tested for reactivity against 191 Epstein-Barr virus transformed B cell lines of the 1000 Genomes Project. By discovering 81 new MiHAs, we more than doubled the antigen repertoire to 159 MiHAs and demonstrated that, despite many genetic differences between patients and donors, often the same MiHAs are targeted in multiple patients. Furthermore, we showed that one quarter of the antigens are cryptic, that is translated from unconventional open reading frames, for example long noncoding RNAs, showing that these antigen types are relevant targets in natural immune responses. Finally, using single cell RNA-seq data, we analyzed tissue expression of MiHA-encoding genes to explore their potential role in clinical outcome, and characterized 11 new hematopoietic-restricted MiHAs as potential targets for immunotherapy. In conclusion, we expanded the repertoire of HLA-I-restricted MiHAs and identified recurrent, cryptic and hematopoietic-restricted antigens, which are fundamental to predict, follow or manipulate immune responses to improve clinical outcome after alloSCT.

Determining epitope specificity of T-cell receptors with transformers.

SUMMARY: T-cell receptors (TCRs) on T cells recognize and bind to epitopes presented by the major histocompatibility complex in case of an infection or cancer. However, the high diversity of TCRs, as well as their unique and complex binding mechanisms underlying epitope recognition, make it difficult to predict the binding between TCRs and epitopes. Here, we present the utility of transformers, a deep learning strategy that incorporates an attention mechanism that learns the informative features, and show that these models pre-trained on a large set of protein sequences outperform current strategies. We compared three pre-trained auto-encoder transformer models (ProtBERT, ProtAlbert, and ProtElectra) and one pre-trained auto-regressive transformer model (ProtXLNet) to predict the binding specificity of TCRs to 25 epitopes from the VDJdb database (human and murine). Two additional modifications were performed to incorporate gene usage of the TCRs in the four transformer models. Of all 12 transformer implementations (four models with three different modifications), a modified version of the ProtXLNet model could predict TCR-epitope pairs with the highest accuracy (weighted F1 score 0.55 simultaneously considering all 25 epitopes). The modification included additional features representing the gene names for the TCRs. We also showed that the basic implementation of transformers outperformed the previously available methods, i.e. TCRGP, TCRdist, and DeepTCR, developed for the same biological problem, especially for the hard-to-classify labels. We show that the proficiency of transformers in attention learning can be made operational in a complex biological setting like TCR binding prediction. Further ingenuity in utilizing the full potential of transformers, either through attention head visualization or introducing additional features, can extend T-cell research avenues. AVAILABILITY AND IMPLEMENTATION: Data and code are available on https://github.com/InduKhatri/tcrformer.

Performance of spectral flow cytometry and mass cytometry for the study of innate myeloid cell populations.

Introduction: Monitoring of innate myeloid cells (IMC) is broadly applied in basic and translational research, as well as in diagnostic patient care. Due to their immunophenotypic heterogeneity and biological plasticity, analysis of IMC populations typically requires large panels of markers. Currently, two cytometry-based techniques allow for the simultaneous detection of ≥40 markers: spectral flow cytometry (SFC) and mass cytometry (MC). However, little is known about the comparability of SFC and MC in studying IMC populations. Methods: We evaluated the performance of two SFC and MC panels, which contained 21 common markers, for the identification and subsetting of blood IMC populations. Based on unsupervised clustering analysis, we systematically identified 24 leukocyte populations, including 21 IMC subsets, regardless of the cytometry technique. Results: Overall, comparable results were observed between the two technologies regarding the relative distribution of these cell populations and the staining resolution of individual markers (Pearson's ρ=0.99 and 0.55, respectively). However, minor differences were observed between the two techniques regarding intra-measurement variability (median coefficient of variation of 42.5% vs. 68.0% in SFC and MC, respectively; p<0.0001) and reproducibility, which were most likely due to the significantly longer acquisition times (median 16 min vs. 159 min) and lower recovery rates (median 53.1% vs. 26.8%) associated with SFC vs. MC. Discussion: Altogether, our results show a good correlation between SFC and MC for the identification, enumeration and characterization of IMC in blood, based on large panels (>20) of antibody reagents.

IL-7 receptor signaling drives human B-cell progenitor differentiation and expansion.

Although absence of interleukin-7 (IL-7) signaling completely abrogates T and B lymphopoiesis in mice, patients with severe combined immunodeficiency caused by mutations in the IL-7 receptor α chain (IL-7Rα) still generate peripheral blood B cells. Consequently, human B lymphopoiesis has been thought to be independent of IL-7 signaling. Using flow cytometric analysis and single-cell RNA sequencing of bone marrow samples from healthy controls and patients who are IL-7Rα deficient, in combination with in vitro modeling of human B-cell differentiation, we demonstrate that IL-7R signaling plays a crucial role in human B lymphopoiesis. IL-7 drives proliferation and expansion of early B-cell progenitors but not of pre-BII large cells and has a limited role in the prevention of cell death. Furthermore, IL-7 guides cell fate decisions by enhancing the expression of BACH2, EBF1, and PAX5, which jointly orchestrate the specification and commitment of early B-cell progenitors. In line with this observation, early B-cell progenitors of patients with IL-7Rα deficiency still expressed myeloid-specific genes. Collectively, our results unveil a previously unknown role for IL-7 signaling in promoting the B-lymphoid fate and expanding early human B-cell progenitors while defining important differences between mice and humans. Our results have implications for hematopoietic stem cell transplantation strategies in patients with T- B+ severe combined immunodeficiency and provide insights into the role of IL-7R signaling in leukemogenesis.

CD4+ helper T cells endow cDC1 with cancer-impeding functions in the human tumor micro-environment.

Despite their low abundance in the tumor microenvironment (TME), classical type 1 dendritic cells (cDC1) play a pivotal role in anti-cancer immunity, and their abundance positively correlates with patient survival. However, their interaction with CD4+ T-cells to potentially enable the cytotoxic T lymphocyte (CTL) response has not been elucidated. Here we show that contact with activated CD4+ T-cells enables human ex vivo cDC1, but no other DC types, to induce a CTL response to cell-associated tumor antigens. Single cell transcriptomics reveals that CD4+ T-cell help uniquely optimizes cDC1 in many functions that support antigen cross-presentation and T-cell priming, while these changes don't apply to other DC types. We robustly identify "helped" cDC1 in the TME of a multitude of human cancer types by the overlap in their transcriptomic signature with that of recently defined, tumor-infiltrating DC states that prove to be positively prognostic. As predicted from the functional effects of CD4+ T-cell help, the transcriptomic signature of "helped" cDC1 correlates with tumor infiltration by CTLs and Thelper(h)-1 cells, overall survival and response to PD-1-targeting immunotherapy. These findings reveal a critical role for CD4+ T-cell help in enabling cDC1 function in the TME and may establish the helped cDC1 transcriptomic signature as diagnostic marker in cancer.

Quantitative proteomics of small numbers of closely-related cells: Selection of the optimal method for a clinical setting.

Mass spectrometry (MS)-based proteomics profiling has undoubtedly increased the knowledge about cellular processes and functions. However, its applicability for paucicellular sample analyses is currently limited. Although new approaches have been developed for single-cell studies, most of them have not (yet) been standardized and/or require highly specific (often home-built) devices, thereby limiting their broad implementation, particularly in non-specialized settings. To select an optimal MS-oriented proteomics approach applicable in translational research and clinical settings, we assessed 10 different sample preparation procedures in paucicellular samples of closely-related cell types. Particularly, five cell lysis protocols using different chemistries and mechanical forces were combined with two sample clean-up techniques (C18 filter- and SP3-based), followed by tandem mass tag (TMT)-based protein quantification. The evaluation was structured in three phases: first, cell lines from hematopoietic (THP-1) and non-hematopoietic (HT-29) origins were used to test the approaches showing the combination of a urea-based lysis buffer with the SP3 bead-based clean-up system as the best performer. Parameters such as reproducibility, accessibility, spatial distribution, ease of use, processing time and cost were considered. In the second phase, the performance of the method was tested on maturation-related cell populations: three different monocyte subsets from peripheral blood and, for the first time, macrophages/microglia (MAC) from glioblastoma samples, together with T cells from both tissues. The analysis of 50,000 cells down to only 2,500 cells revealed different protein expression profiles associated with the distinct cell populations. Accordingly, a closer relationship was observed between non-classical monocytes and MAC, with the latter showing the co-expression of M1 and M2 macrophage markers, although pro-tumoral and anti-inflammatory proteins were more represented. In the third phase, the results were validated by high-end spectral flow cytometry on paired monocyte/MAC samples to further determine the sensitivity of the MS approach selected. Finally, the feasibility of the method was proven in 194 additional samples corresponding to 38 different cell types, including cells from different tissue origins, cellular lineages, maturation stages and stimuli. In summary, we selected a reproducible, easy-to-implement sample preparation method for MS-based proteomic characterization of paucicellular samples, also applicable in the setting of functionally closely-related cell populations.

Development of a standardized and validated flow cytometry approach for monitoring of innate myeloid immune cells in human blood.

Innate myeloid cell (IMC) populations form an essential part of innate immunity. Flow cytometric (FCM) monitoring of IMCs in peripheral blood (PB) has great clinical potential for disease monitoring due to their role in maintenance of tissue homeostasis and ability to sense micro-environmental changes, such as inflammatory processes and tissue damage. However, the lack of standardized and validated approaches has hampered broad clinical implementation. For accurate identification and separation of IMC populations, 62 antibodies against 44 different proteins were evaluated. In multiple rounds of EuroFlow-based design-testing-evaluation-redesign, finally 16 antibodies were selected for their non-redundancy and separation power. Accordingly, two antibody combinations were designed for fast, sensitive, and reproducible FCM monitoring of IMC populations in PB in clinical settings (11-color; 13 antibodies) and translational research (14-color; 16 antibodies). Performance of pre-analytical and analytical variables among different instruments, together with optimized post-analytical data analysis and reference values were assessed. Overall, 265 blood samples were used for design and validation of the antibody combinations and in vitro functional assays, as well as for assessing the impact of sample preparation procedures and conditions. The two (11- and 14-color) antibody combinations allowed for robust and sensitive detection of 19 and 23 IMC populations, respectively. Highly reproducible identification and enumeration of IMC populations was achieved, independently of anticoagulant, type of FCM instrument and center, particularly when database/software-guided automated (vs. manual "expert-based") gating was used. Whereas no significant changes were observed in identification of IMC populations for up to 24h delayed sample processing, a significant impact was observed in their absolute counts after >12h delay. Therefore, accurate identification and quantitation of IMC populations requires sample processing on the same day. Significantly different counts were observed in PB for multiple IMC populations according to age and sex. Consequently, PB samples from 116 healthy donors (8-69 years) were used for collecting age and sex related reference values for all IMC populations. In summary, the two antibody combinations and FCM approach allow for rapid, standardized, automated and reproducible identification of 19 and 23 IMC populations in PB, suited for monitoring of innate immune responses in clinical and translational research settings.

pmTR database: population matched (pm) germline allelic variants of T-cell receptor (TR) loci.

The IMGT database profiles the TR germline alleles for all four TR loci (TRA, TRB, TRG and TRD), however, it does not comprise of the information regarding population specificity and allelic frequencies of these germline alleles. The specificity of allelic variants to different human populations can, however, be a rich source of information when studying the genetic basis of population-specific immune responses in disease and in vaccination. Therefore, we meticulously identified true germline alleles enriched with complete TR allele sequences and their frequencies across 26 different human populations, profiled by "1000 Genomes data". We identified 205 TRAV, 249 TRBV, 16 TRGV and 5 TRDV germline alleles supported by at least four haplotypes. The diversity of germline allelic variants in the TR loci is the highest in Africans, while the majority of the Non-African alleles are specific to the Asian populations, suggesting a diverse profile of TR germline alleles in different human populations. Interestingly, the alleles in the IMGT database are frequent and common across all five super-populations. We believe that this new set of germline TR sequences represents a valuable new resource which we have made available through the new population-matched TR (pmTR) database, accessible via https://pmtrig.lumc.nl/ .

Longitudinal Dynamics of Human B-Cell Response at the Single-Cell Level in Response to Tdap Vaccination.

To mount an adequate immune response against pathogens, stepwise mutation and selection processes are crucial functions of the adaptive immune system. To better characterize a successful vaccination response, we performed longitudinal (days 0, 5, 7, 10, and 14 after Boostrix vaccination) analysis of the single-cell transcriptome as well as the B-cell receptor (BCR) repertoire (scBCR-rep) in plasma cells of an immunized donor and compared it with baseline B-cell characteristics as well as flow cytometry findings. Based on the flow cytometry knowledge and literature findings, we discriminated individual B-cell subsets in the transcriptomics data and traced over-time maturation of plasmablasts/plasma cells (PB/PCs) and identified the pathways associated with the plasma cell maturation. We observed that the repertoire in PB/PCs differed from the baseline B-cell repertoire e.g., regarding expansion of unique clones in post-vaccination visits, high usage of IGHG1 in expanded clones, increased class-switching events post-vaccination represented by clonotypes spanning multiple IGHC classes and positive selection of CDR3 sequences over time. Importantly, the Variable gene family-based clustering of BCRs represented a similar measure as the gene-based clustering, but certainly improved the clustering of BCRs, as BCRs from duplicated Variable gene families could be clustered together. Finally, we developed a query tool to dissect the immune response to the components of the Boostrix vaccine. Using this tool, we could identify the BCRs related to anti-tetanus and anti-pertussis toxoid BCRs. Collectively, we developed a bioinformatic workflow which allows description of the key features of an ongoing (longitudinal) immune response, such as activation of PB/PCs, Ig class switching, somatic hypermutation, and clonal expansion, all of which are hallmarks of antigen exposure, followed by mutation & selection processes.

Highly Sensitive Flow Cytometry Allows Monitoring of Changes in Circulating Immune Cells in Blood After Tdap Booster Vaccination.

Antigen-specific serum immunoglobulin (Ag-specific Ig) levels are broadly used as correlates of protection. However, in several disease and vaccination models these fail to predict immunity. In these models, in-depth knowledge of cellular processes associated with protective versus poor responses may bring added value. We applied high-throughput multicolor flow cytometry to track over-time changes in circulating immune cells in 10 individuals following pertussis booster vaccination (Tdap, Boostrix®, GlaxoSmithKline). Next, we applied correlation network analysis to extensively investigate how changes in individual cell populations correlate with each other and with Ag-specific Ig levels. We further determined the most informative cell subsets and analysis time points for future studies. Expansion and maturation of total IgG1 plasma cells, which peaked at day 7 post-vaccination, was the most prominent cellular change. Although these cells preceded the increase in Ag-specific serum Ig levels, they did not correlate with the increase of Ig levels. In contrast, strong correlation was observed between Ag-specific IgGs and maximum expansion of total IgG1 and IgA1 memory B cells at days 7 to 28. Changes in circulating T cells were limited, implying the need for a more sensitive approach. Early changes in innate immune cells, i.e. expansion of neutrophils, and expansion and maturation of monocytes up to day 5, most likely reflected their responses to local damage and adjuvant. Here we show that simultaneous monitoring of multiple circulating immune subsets in blood by flow cytometry is feasible. B cells seem to be the best candidates for vaccine monitoring.

Population matched (pm) germline allelic variants of immunoglobulin (IG) loci: Relevance in infectious diseases and vaccination studies in human populations.

Immunoglobulin (IG) loci harbor inter-individual allelic variants in many different germline IG variable, diversity and joining genes of the IG heavy (IGH), kappa (IGK) and lambda (IGL) loci, which together form the genetic basis of the highly diverse antigen-specific B-cell receptors. These allelic variants can be shared between or be specific to human populations. The current immunogenetics resources gather the germline alleles, however, lack the population specificity of the alleles which poses limitations for disease-association studies related to immune responses in different human populations. Therefore, we systematically identified germline alleles from 26 different human populations around the world, profiled by "1000 Genomes" data. We identified 409 IGHV, 179 IGKV, and 199 IGLV germline alleles supported by at least seven haplotypes. The diversity of germline alleles is the highest in Africans. Remarkably, the variants in the identified novel alleles show strikingly conserved patterns, the same as found in other IG databases, suggesting over-time evolutionary selection processes. We could relate the genetic variants to population-specific immune responses, e.g. IGHV1-69 for flu in Africans. The population matched IG (pmIG) resource will enhance our understanding of the SHM-related B-cell receptor selection processes in (infectious) diseases and vaccination within and between different human populations.

Corrigendum: Blocking of the High-Affinity Interaction-Synapse Between SARS-CoV-2 Spike and Human ACE2 Proteins Likely Requires Multiple High-Affinity Antibodies: An Immune Perspective.

[This corrects the article DOI: 10.3389/fimmu.2020.570018.].

A Transcriptomics-Based Meta-Analysis Combined With Machine Learning Identifies a Secretory Biomarker Panel for Diagnosis of Pancreatic Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is generally incurable due to the late diagnosis and absence of markers that are concordant with expression in several sample sources (i.e., tissue, blood, plasma) and platforms (i.e., Microarray, sequencing). We optimized meta-analysis of 19 PDAC (tissue and blood) transcriptome studies from multiple platforms. The key biomarkers for PDAC diagnosis with secretory potential were identified and validated in different cohorts. Machine learning approach i.e., support vector machine supported by leave-one-out cross-validation was used to build and test the classifier. We identified a 9-gene panel (IFI27, ITGB5, CTSD, EFNA4, GGH, PLBD1, HTATIP2, IL1R2, CTSA) that achieved ∼0.92 average sensitivity and ∼0.90 average specificity in distinguishing PDAC from healthy samples in five training sets using cross-validation. These markers were also validated in proteomics and single-cell transcriptomics studies suggesting their prognostic role in the diagnosis of PDAC. Our 9-gene classifier can not only clearly discriminate between better and poor survivors but can also precisely discriminate PDAC from chronic pancreatitis (AUC = 0.95), early stages of progression [Stage I and II (AUC = 0.82), IPMA and IPMN (AUC = 1), and IPMC (AUC = 0.81)]. The 9-gene marker outperformed the previously known markers in blood studies particularly (AUC = 0.84). The discrimination of PDAC from early precursor lesions in non-malignant tissue (AUC > 0.81) and peripheral blood (AUC > 0.80) may assist in an early diagnosis of PDAC in blood samples and thus will also facilitate risk stratification upon validation in clinical trials.

Blocking of the High-Affinity Interaction-Synapse Between SARS-CoV-2 Spike and Human ACE2 Proteins Likely Requires Multiple High-Affinity Antibodies: An Immune Perspective.

The pandemic of Coronavirus Disease 2019 (COVID-19) caused by SARS-CoV-2 has induced global eagerness to develop vaccines and therapeutics for treating COVID-19, including neutralizing antibodies. To develop effective therapeutic antibodies against SARS-CoV-2, it is critical to understand the interaction between viral and host's proteins. The human ACE2 (hACE2) protein is the crucial target for the SARS-CoV's Spike protein that allows the virus to adhere to host epithelial cells. X-ray crystal structures and biophysical properties of protein-protein interactions reveal a large interaction surface with high binding-affinity between SARS-CoV-2 and hACE2 (18 interactions), at least 15-fold stronger than between SARS-CoV-1 and hACE2 (eight interactions). This suggests that antibodies against CoV-1 infection might not be very efficient against CoV-2. Furthermore, interspecies comparisons indicate that ACE2 proteins of man and cat are far closer than dog, ferret, mouse, and rat with significant differences in binding-affinity between Spike and ACE2 proteins. This strengthens the notion of productive SARS-CoV-2 transmission between felines and humans and that classical animal models are not optimally suited for evaluating therapeutic antibodies. The large interaction surface with strong affinity between SARS-CoV-2 and hACE2 (dG-12.4) poses a huge challenge to develop reliable antibody therapy that truly blocks SARS-CoV-2 adherence and infection. We gauge that single antibodies against single epitopes might not sufficiently interfere with the strong interaction-synapse between Spike and hACE2 proteins. Instead, appropriate combinations of high-affinity neutralizing antibodies against different epitopes might be needed, preferably of IgA-class for optimal and prolonged activity at epithelial layers of respiratory and intestine tracts.

Metagenomics analysis reveals features unique to Indian distal gut microbiota.

Various factors including diet, age, geography, culture and socio-economic status have a role in determining the composition of the human gut microbiota. The human gut microbial composition is known to be altered in disease conditions. Considering the important role of the gut microbiome in maintaining homeostasis and overall health, it is important to understand the microbial diversity and the functional metagenome of the healthy gut. Here, we characterized the microbiota of 31 fecal samples from healthy individuals of Indian ethnic tribes from Ladakh, Jaisalmer and Khargone by shotgun metagenomic sequencing. Sequence analysis revealed that Bifidobacterium and Prevotella were the key microbes contributing to the differences among Jaisalmer, Khargone and Ladakh samples at the genus level. Our correlation network study identified carbohydrate-active enzymes and carbohydrate binding proteins that are associated with specific genera in the different Indian geographical regions studied. Network analysis of carbohydrate-active enzymes and genus abundance revealed that the presence of different carbohydrate-active enzymes is driven by differential abundance of genera. The correlation networks were different in the different geographical regions, and these interactions suggest the role of less abundant genera in shaping the gut environment. We compared our data with samples from different countries and found significant differences in taxonomic composition and abundance of carbohydrate-active enzymes in the gut microbiota as compared to the other countries.

Eleven grand challenges in single-cell data science.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Taxonomic insights into the phylogeny of Bacillus badius and proposal for its reclassification to the genus Pseudobacillus as Pseudobacillus badius comb. nov. and reclassification of Bacillus wudalianchiensis Liu et al., 2017 as Pseudobacillus wudalianchiensis comb. nov.

The species Bacillus badius is one of the oldest members of the genus Bacillus isolated from faeces of children and was classified based on its ability to form endospores [8]. In 16S rRNA gene sequence and phylogenetic analysis, Bacillus badius DSM 23T shared low similarity (93.0%) and distant relationship with B. subtilis, the type species of the genus Bacillus indicating that it does not belong to this genus. Additional strains of the species, B. badius DSM 5610, DSM 30822 and B. encimensis SGD-V-25 (which has been recently reclassified as a member of this species) were included in the study to consider intraspecies diversity. Detailed molecular phylogenetic and comparative genome analysis clearly showed that the strains of B. badius were consistently retrieved outside the cluster of Bacillus sensu stricto and also distantly related to the genera Domibacillus and Quasibacillus. Further, the data from biochemical reactions (inability to ferment most carbohydrates), polar lipids profile (presence of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine and an aminophosphoglycolipid) and fatty acids supported the molecular analysis. Thus the four B. badius strains; DSM 23T, DSM 5610, DSM 30822 and SGD-V-25 displayed sufficient demarcating phenotypic characteristics that warrant their classification as members of a novel genus and single species, for which the name Pseudobacillus badius gen. nov. comb. nov. is proposed with Pseudobacillus badius DSM 23T (= ATCC 14574T) as the type strain. Additionally, based on our findings from phenotypic, chemotaxonomic and genotypic parameters, Bacillus wudalianchiensis DSM 100757T was reclassified as Pseudobacillus wudalianchiensis comb. nov.

Systems Biology Approach to Identify Novel Genomic Determinants for Pancreatic Cancer Pathogenesis.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with a 5-year survival rate of <8%. Its dismal prognosis stems from inefficient therapeutic modalities owing to the lack of understanding about pancreatic cancer pathogenesis. Considering the molecular complexity and heterogeneity of PDAC, identification of novel molecular contributors involved in PDAC onset and progression using global "omics" analysis will pave the way to improved strategies for disease prevention and therapeutic targeting. Meta-analysis of multiple miRNA microarray datasets containing healthy controls (HC), chronic pancreatitis (CP) and PDAC cases, identified 13 miRNAs involved in the progression of PDAC. These miRNAs showed dysregulation in both tissue as well as blood samples, along with progressive decrease in expression from HC to CP to PDAC. Gene-miRNA interaction analysis further elucidated 5 miRNAs (29a/b, 27a, 130b and 148a) that are significantly downregulated in conjunction with concomitant upregulation of their target genes throughout PDAC progression. Among these, miRNA-29a/b targeted genes were found to be most significantly altered in comparative profiling of HC, CP and PDAC, indicating its involvement in malignant evolution. Further, pathway analysis suggested direct involvement of miRNA-29a/b in downregulating the key pathways associated with PDAC development and metastasis including focal adhesion signaling and extracellular matrix organization. Our systems biology data analysis, in combination with real-time PCR validation indicates direct functional involvement of miRNA-29a in PDAC progression and is a potential prognostic marker and therapeutic candidate for patients with progressive disease.

Composite genome sequence of Bacillus clausii, a probiotic commercially available as Enterogermina®, and insights into its probiotic properties.

BACKGROUND: Some of the spore-forming strains of Bacillus probiotics are marketed commercially as they survive harsh gastrointestinal conditions and bestow health benefits to the host. RESULTS: We report the composite genome of Bacillus clausii ENTPro from a commercially available probiotic Enterogermina® and compare it with the genomes of other Bacillus probiotics. We find that the members of B. clausii species harbor high heterogeneity at the species as well as genus level. The genes conferring resistance to chloramphenicol, streptomycin, rifampicin, and tetracycline in the B. clausii ENTPro strain could be identified. The genes coding for the bacteriocin gallidermin, which prevents biofilm formation in the pathogens Staphylococcus aureus and S. epidermidis, were also identified. KEGG Pathway analysis suggested that the folate biosynthesis pathway, which depicts one of the important roles of probiotics in the host, is conserved completely in B. subtilis and minimally in B. clausii and other probiotics. CONCLUSIONS: We identified various antibiotic resistance, bacteriocins, stress-related, and adhesion-related domains, and industrially-relevant pathways, in the genomes of these probiotic bacteria that are likely to help them survive in the harsh gastrointestinal tract, facilitating adhesion to host epithelial cells, persistence during antibiotic treatment and combating bacterial infections.