An atlas of human vector-borne microbe interactions reveals pathogenicity mechanisms.

Vector-borne diseases are a leading cause of death worldwide and pose a substantial unmet medical need. Pathogens binding to host extracellular proteins (the "exoproteome") represents a crucial interface in the etiology of vector-borne disease. Here, we used bacterial selection to elucidate host-microbe interactions in high throughput (BASEHIT)-a technique enabling interrogation of microbial interactions with 3,324 human exoproteins-to profile the interactomes of 82 human-pathogen samples, including 30 strains of arthropod-borne pathogens and 8 strains of related non-vector-borne pathogens. The resulting atlas revealed 1,303 putative interactions, including hundreds of pairings with potential roles in pathogenesis, including cell invasion, tissue colonization, immune evasion, and host sensing. Subsequent functional investigations uncovered that Lyme disease spirochetes recognize epidermal growth factor as an environmental cue of transcriptional regulation and that conserved interactions between intracellular pathogens and thioredoxins facilitate cell invasion. In summary, this interactome atlas provides molecular-level insights into microbial pathogenesis and reveals potential host-directed targets for next-generation therapeutics.

CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction.

CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture and medicine. However, such power does not come without its own peril, and one such issue is the potential for unintended modifications (Off-Target), which highlights the need for accurate prediction and mitigation strategies. Though previous studies have demonstrated improvement in Off-Target prediction capability with the application of deep learning, they often struggle with the precision-recall trade-off, limiting their effectiveness and do not provide proper interpretation of the complex decision-making process of their models. To address these limitations, we have thoroughly explored deep learning networks, particularly the recurrent neural network based models, leveraging their established success in handling sequence data. Furthermore, we have employed genetic algorithm for hyperparameter tuning to optimize these models' performance. The results from our experiments demonstrate significant performance improvement compared with the current state-of-the-art in Off-Target prediction, highlighting the efficacy of our approach. Furthermore, leveraging the power of the integrated gradient method, we make an effort to interpret our models resulting in a detailed analysis and understanding of the underlying factors that contribute to Off-Target predictions, in particular the presence of two sub-regions in the seed region of single guide RNA which extends the established biological hypothesis of Off-Target effects. To the best of our knowledge, our model can be considered as the first model combining high efficacy, interpretability and a desirable balance between precision and recall.

PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information.

The dynamic evolution of the severe acute respiratory syndrome coronavirus 2 virus is primarily driven by mutations in its genetic sequence, culminating in the emergence of variants with increased capability to evade host immune responses. Accurate prediction of such mutations is fundamental in mitigating pandemic spread and developing effective control measures. This study introduces a robust and interpretable deep-learning approach called PRIEST. This innovative model leverages time-series viral sequences to foresee potential viral mutations. Our comprehensive experimental evaluations underscore PRIEST's proficiency in accurately predicting immune-evading mutations. Our work represents a substantial step in utilizing deep-learning methodologies for anticipatory viral mutation analysis and pandemic response.

A repeating fast radio burst source localized to a nearby spiral galaxy.

Fast radio bursts (FRBs) are brief, bright, extragalactic radio flashes1,2. Their physical origin remains unknown, but dozens of possible models have been postulated3. Some FRB sources exhibit repeat bursts4-7. Although over a hundred FRB sources have been discovered8, only four have been localized and associated with a host galaxy9-12, and just one of these four is known to emit repeating FRBs9. The properties of the host galaxies, and the local environments of FRBs, could provide important clues about their physical origins. The first known repeating FRB, however, was localized to a low-metallicity, irregular dwarf galaxy, and the apparently non-repeating sources were localized to higher-metallicity, massive elliptical or star-forming galaxies, suggesting that perhaps the repeating and apparently non-repeating sources could have distinct physical origins. Here we report the precise localization of a second repeating FRB source6, FRB 180916.J0158+65, to a star-forming region in a nearby (redshift 0.0337 ± 0.0002) massive spiral galaxy, whose properties and proximity distinguish it from all known hosts. The lack of both a comparably luminous persistent radio counterpart and a high Faraday rotation measure6 further distinguish the local environment of FRB 180916.J0158+65 from that of the single previously localized repeating FRB source, FRB 121102. This suggests that repeating FRBs may have a wide range of luminosities, and originate from diverse host galaxies and local environments.

Succinylated lysine residue prediction revisited.

Lysine succinylation is a kind of post-translational modification (PTM) that plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The experimental methods to detect succinylation sites are time-consuming and costly. This thus calls for computational models with high efficacy, and attention has been given in the literature to develop such models, albeit with only moderate success in the context of different evaluation metrics. One crucial aspect in this context is the biochemical and physicochemical properties of amino acids, which appear to be useful as features for such computational predictors. However, some of the existing computational models did not use the biochemical and physicochemical properties of amino acids. In contrast, some others used them without considering the inter-dependency among the properties. The combinations of biochemical and physicochemical properties derived through our optimization process achieve better results than the results achieved by combining all the properties. We propose three deep learning architectures: CNN+Bi-LSTM (CBL), Bi-LSTM+CNN (BLC) and their combination (CBL_BLC). We find that CBL_BLC outperforms the other two. Ensembling of different models successfully improves the results. Notably, tuning the threshold of the ensemble classifiers further improves the results. Upon comparing our work with other existing works on two datasets, we successfully achieve better sensitivity and specificity by varying the threshold value.

StackDPP: a stacking ensemble based DNA-binding protein prediction model.

BACKGROUND: DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DNA. Very few proteins, however, are DNA-binding in nature. Therefore, it is necessary to develop an efficient predictor for identifying DNA-BPs. RESULT: In this work, we have proposed new benchmark datasets for the DNA-binding protein prediction problem. We discovered several quality concerns with the widely used benchmark datasets, PDB1075 (for training) and PDB186 (for independent testing), which necessitated the preparation of new benchmark datasets. Our proposed datasets UNIPROT1424 and UNIPROT356 can be used for model training and independent testing respectively. We have retrained selected state-of-the-art DNA-BP predictors in the new dataset and reported their performance results. We also trained a novel predictor using the new benchmark dataset. We extracted features from various feature categories, then used a Random Forest classifier and Recursive Feature Elimination with Cross-validation (RFECV) to select the optimal set of 452 features. We then proposed a stacking ensemble architecture as our final prediction model. Named Stacking Ensemble Model for DNA-binding Protein Prediction, or StackDPP in short, our model achieved 0.92, 0.92 and 0.93 accuracy in 10-fold cross-validation, jackknife and independent testing respectively. CONCLUSION: StackDPP has performed very well in cross-validation testing and has outperformed all the state-of-the-art prediction models in independent testing. Its performance scores in cross-validation testing generalized very well in the independent test set. The source code of the model is publicly available at https://github.com/HasibAhmed1624/StackDPP . Therefore, we expect this generalized model can be adopted by researchers and practitioners to identify novel DNA-binding proteins.

Ceramides Increase Fatty Acid Utilization in Intestinal Progenitors to Enhance Stemness and Increase Tumor Risk.

BACKGROUND & AIMS: Cancers of the alimentary tract, including esophageal adenocarcinomas, colorectal cancers, and cancers of the gastric cardia, are common comorbidities of obesity. Prolonged, excessive delivery of macronutrients to the cells lining the gut can increase one's risk for these cancers by inducing imbalances in the rate of intestinal stem cell proliferation vs differentiation, which can produce polyps and other aberrant growths. We investigated whether ceramides, which are sphingolipids that serve as a signal of nutritional excess, alter stem cell behaviors to influence cancer risk. METHODS: We profiled sphingolipids and sphingolipid-synthesizing enzymes in human adenomas and tumors. Thereafter, we manipulated expression of sphingolipid-producing enzymes, including serine palmitoyltransferase (SPT), in intestinal progenitors of mice, cultured organoids, and Drosophila to discern whether sphingolipids altered stem cell proliferation and metabolism. RESULTS: SPT, which diverts dietary fatty acids and amino acids into the biosynthetic pathway that produces ceramides and other sphingolipids, is a critical modulator of intestinal stem cell homeostasis. SPT and other enzymes in the sphingolipid biosynthesis pathway are up-regulated in human intestinal adenomas. They produce ceramides, which serve as prostemness signals that stimulate peroxisome-proliferator activated receptor-α and induce fatty acid binding protein-1. These actions lead to increased lipid utilization and enhanced proliferation of intestinal progenitors. CONCLUSIONS: Ceramides serve as critical links between dietary macronutrients, epithelial regeneration, and cancer risk.

NoVaTeST: identifying genes with location-dependent noise variance in spatial transcriptomics data.

MOTIVATION: Spatial transcriptomics (ST) can reveal the existence and extent of spatial variation of gene expression in complex tissues. Such analyses could help identify spatially localized processes underlying a tissue's function. Existing tools to detect spatially variable genes assume a constant noise variance across spatial locations. This assumption might miss important biological signals when the variance can change across locations. RESULTS: In this article, we propose NoVaTeST, a framework to identify genes with location-dependent noise variance in ST data. NoVaTeST models gene expression as a function of spatial location and allows the noise to vary spatially. NoVaTeST then statistically compares this model to one with constant noise and detects genes showing significant spatial noise variation. We refer to these genes as "noisy genes." In tumor samples, the noisy genes detected by NoVaTeST are largely independent of the spatially variable genes detected by existing tools that assume constant noise, and provide important biological insights into tumor microenvironments. AVAILABILITY AND IMPLEMENTATION: An implementation of the NoVaTeST framework in Python along with instructions for running the pipeline is available at https://github.com/abidabrar-bracu/NoVaTeST.

ScribbleDom: using scribble-annotated histology images to identify domains in spatial transcriptomics data.

MOTIVATION: Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. RESULTS: In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot microenvironment through convolution filters of varying sizes, in the form of "Inception" blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods-between 1.82% to 169.38% improvements in adjusted Rand index for 9 of the 12 human dorsolateral prefrontal cortex samples, and 15.54% improvement in the melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. AVAILABILITY AND IMPLEMENTATION: Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669).

Crowdsourcing to increase hepatitis B and C testing and reduce hepatitis stigma among medical students in Bangladesh.

This study addresses the pervasive challenges of low hepatitis B (HBV) and hepatitis C (HCV) testing rates coupled with the stigma associated with these diseases in low- and middle-income countries (LMICs) with a special focus on Bangladesh. This study aims to introduce an innovative crowdsourcing intervention that involves medical students, a crucial cohort with the potential to shape healthcare attitudes. Through a structured crowdsourcing approach, the study designs and implements a digital intervention to counter stigma and promote testing among medical students in Dhaka, Bangladesh. Participants submitted brief videos or texts aiming to encourage hepatitis testing and reduce stigma. The call, advertised through meetings, emails, and social media, welcomed entries in English or Bengali over 3 weeks. A panel of six judges evaluated each entry based on clarity, impact potential, innovation, feasibility, and sustainability, awarding prizes to students behind the highest-rated submissions. Seventeen videos and four text messages received an average score of 5.5 among 440 surveyed medical students, predominantly 22 years old (16%) and in their fourth year (21%). After viewing, 360 students underwent screening, identifying two previously undiagnosed HBV cases referred for care; no HCV infections were found. Notably, 41% expressed concerns about individuals with HBV working in hospitals or having a doctor living with HBV. In conclusion, this pilot showcases the power of medical students in spearheading campaigns to counter hepatitis stigma and encourage testing. By utilizing crowdsourcing, the study introduces an innovative approach to a persistent issue in LMICs specially in Bangladesh, offering a model that could potentially be adapted by other regions grappling with similar challenges.

Phytoremediation: a transgenic perspective in omics era.

Phytoremediation is an environmental safety strategy that might serve as a viable preventative approach to reduce soil contamination in a cost-effective manner. Using plants to remediate pollution from the environment is referred to as phytoremediation. In the past few decades, plants have undergone genetic manipulation to overcome inherent limitations by using genetically modified plants. This review illustrates the eco-friendly process of cleaning the environment using transgenic strategies combined with omics technologies. Herbicides tolerance and phytoremediation abilities have been established in genetically modified plants. Transgenic plants have eliminated the pesticides atrazine and metolachlor from the soil. To expand the application of genetically engineered plants for phytoremediation process, it is essential to test strategies in the field and have contingency planning. Omics techniques were used for understanding various genetic, hormonal, and metabolic pathways responsible for phytoremediation in soil. Transcriptomics and metabolomics provide useful information as resources to understand the mechanisms behind phytoremediation. This review aims to highlight the integration of transgenic strategies and omics technologies to enhance phytoremediation efficiency, emphasizing the need for field testing and comprehensive planning for successful implementation.

SARS-CoV-2 outbreak: role of viral proteins and genomic diversity in virus infection and COVID-19 progression.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is the cause of coronavirus disease 2019 (COVID-19); a severe respiratory distress that has emerged from the city of Wuhan, Hubei province, China during December 2019. COVID-19 is currently the major global health problem and the disease has now spread to most countries in the world. COVID-19 has profoundly impacted human health and activities worldwide. Genetic mutation is one of the essential characteristics of viruses. They do so to adapt to their host or to move to another one. Viral genetic mutations have a high potentiality to impact human health as these mutations grant viruses unique unpredicted characteristics. The difficulty in predicting viral genetic mutations is a significant obstacle in the field. Evidence indicates that SARS-CoV-2 has a variety of genetic mutations and genomic diversity with obvious clinical consequences and implications. In this review, we comprehensively summarized and discussed the currently available knowledge regarding SARS-CoV-2 outbreaks with a fundamental focus on the role of the viral proteins and their mutations in viral infection and COVID-19 progression. We also summarized the clinical implications of SARS-CoV-2 variants and how they affect the disease severity and hinder vaccine development. Finally, we provided a massive phylogenetic analysis of the spike gene of 214 SARS-CoV-2 isolates from different geographical regions all over the world and their associated clinical implications.

Aquincola agrisoli sp. nov., isolated from rhizospheric soil of eggplant and in silico genome mining for the prediction of biosynthetic gene clusters.

A Gram-stain negative, aerobic, rod-shaped, motile and flagellated novel bacterial strain, designated MAHUQ-54T, was isolated from the rhizospheric soil of eggplant. The colonies were observed to be light pink coloured, smooth, spherical and 0.2-0.6 mm in diameter when grown on R2A agar medium for 2 days. MAHUQ-54T was able to grow at 15-40 °C, at pH 5.5-9.0 and in the presence of 0-0.5 % NaCl (w/v). The strain gave positive results for both catalase and oxidase tests. The strain was positive for hydrolysis of l-tyrosine, urea, Tween 20 and Tween 80. On the basis of the results of 16S rRNA gene sequence comparisons, the isolate was identified as a member of the genus Aquincola and is closely related to Aquincola tertiaricarbonis L10T (98.8 % sequence similarity) and Leptothrix mobilis Feox-1T (98.2 %). MAHUQ-54T has a draft genome size of 5 994 516 bp (60 contigs), annotated with 5348 protein-coding genes, 45 tRNA and 5 rRNA genes. The average nucleotide identity (ANI) and digital DNA-DNA hybridisation (dDDH) values between MAHUQ-54T and its closest phylogenetic neighbours were 75.8-83.3 and 20.8-25.3 %, respectively. In silico genome mining revealed that MAHUQ-54T has a significant potential for the production of novel natural products in the future. The genomic DNA G+C content was determined to be 70.4 %. The predominant isoprenoid quinone was ubiquinone-8. The major fatty acids were identified as C16  :  0, summed feature 3 (comprising C16  :  1ω7c and/or C16  :  1ω6c) and summed feature 8 (comprising C18  :  1ω7c and/or C18  :  1ω6c). On the basis of dDDH, ANI value, genotypic analysis, chemotaxonomic and physiological data, strain MAHUQ-54T represents a novel species within the genus Aquincola, for which the name Aquincola agrisoli sp. nov. is proposed, with MAHUQ-54T (=KACC 22001T = CGMCC 1.18515T) as the type strain.

Nocardioides agri sp. nov., isolated from garden soil.

A novel bacterial strain, designated as MAH-18T, was isolated from soil sampled in a flower garden. Cells of strain MAH-18T were Gram-stain-positive, aerobic, motile, and rod-shaped. The colonies were beige in colour, smooth, and spherical when grown on Reasoner's 2A agar medium. Strain MAH-18T grew at 20-40 °C, pH 6.0-8.0, and 0-1.0 % NaCl. Cells were able to hydrolyse aesculin, gelatin, and Tween 20. According to the 16S rRNA gene sequence comparisons, the isolate was determined to be a member of the genus Nocardioides and most closely related to Nocardioides pyridinolyticus OS4T (97.9 %), Nocardioides hankookensis DS-30T (97.9 %), Nocardioides aquiterrae GW-9T (97.6 %), Nocardioides soli mbc-2T (97.5 %), Nocardioides conyzicola HWE 2-02T (97.4 %), and Nocardioides mangrovi GBK3QG-3T (96.3 %). Strain MAH-18T has a draft genome size of 4 788 325 bp (eight contigs), 4572 protein-coding genes, 46 tRNA, and three rRNA genes. The average nucleotide identity and digital DNA-DNA hybridization values between strain MAH-18T and the closest type strains were 81.5-83.4 % and 24.4-25.8 %, respectively. In silico genome mining revealed several biosynthetic gene clusters in the genome of the novel strain MAH-18T. The G+C content of the genomic DNA of strain was 72.2 mol% and the predominant isoprenoid quinone was MK-8 (H4). The main polar lipids were phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine, and unknown phospholipids. The major cellular fatty acids were determined to be C16:0 iso and C17 : 1 ω6c. The DNA-DNA hybridization results and phenotypic, genotypic, and chemotaxonomic data demonstrated that strain MAH-18T represents a novel species, for which the name Nocardioides agri sp. nov. is proposed, with MAH-18T as the type strain (=KACC 19744T=CGMCC 1.13656T).

A Canadian survey of perceptions and practices related to ordering of blood tests in the intensive care unit.

PURPOSE: The ordering of routine blood test panels in advance is common in intensive care units (ICUs), with limited consideration of the pretest probability of finding abnormalities. This practice contributes to anemia, false positive results, and health care costs. We sought to understand practices and attitudes of Canadian adult intensivists regarding ordering of blood tests in critically ill patients. METHODS: We conducted a nationwide Canadian cross-sectional survey consisting of 15 questions assessing three domains (global perceptions, test ordering, daily practice), plus 11 demographic questions. The target sample was one intensivist per adult ICU in Canada. We summarized responses using descriptive statistics and present data as mean with standard deviation (SD) or count with percentage as appropriate. RESULTS: Over seven months, 80/131 (61%) physicians responded from 77 ICUs, 50% of which were from Ontario. Respondents had a mean (SD) clinical experience of 12 (9) years, and 61% worked in academic centres. When asked about their perceptions of how frequently unnecessary blood tests are ordered, 61% responded "sometimes" and 23% responded "almost always." Fifty-seven percent favoured ordering complete blood counts one day in advance. Only 24% of respondents believed that advanced blood test ordering frequently led to changes in management. The most common factors perceived to influence blood test ordering in the ICU were physician preferences, institutional patterns, and order sets. CONCLUSION: Most respondents to this survey perceived that unnecessary blood testing occurs in the ICU. The survey identified possible strategies to decrease the number of blood tests.

RéSUMé: OBJECTIF: La prescription à l'avance de tests sanguins de routine est courante dans les unités de soins intensifs (USI), avec une prise en compte limitée de la probabilité de découverte d'anomalies avant le test. Cette pratique contribue à l'anémie, aux résultats faussement positifs et aux coûts des soins de santé. Nous avons cherché à comprendre les pratiques et les attitudes des intensivistes pour adultes au Canada en ce qui concerne la prescription d'analyses sanguines chez la patientèle gravement malade. MéTHODE: Nous avons mené un sondage transversal à l'échelle nationale au Canada en posant 15 questions évaluant trois domaines (perceptions globales, commande de tests, pratique quotidienne), ainsi que 11 questions démographiques. L'échantillon cible était composé d'un·e intensiviste par unité de soins intensifs pour adultes au Canada. Nous avons résumé les réponses à l'aide de statistiques descriptives et présenté les données sous forme de moyennes avec écarts type (ET) ou de dénombrements avec pourcentages, selon le cas. RéSULTATS: Sur une période de sept mois, 80 médecins sur 131 (61%) ont répondu dans 77 unités de soins intensifs, dont 50% en Ontario. Les répondant·es avaient une expérience clinique moyenne (ET) de 12 (9) ans, et 61% travaillaient dans des centres universitaires. Lorsqu'on leur a demandé ce qu'ils ou elles pensaient de la fréquence à laquelle des tests sanguins inutiles étaient prescrits, 61% ont répondu « parfois ¼ et 23% ont répondu « presque toujours ¼. Cinquante-sept pour cent étaient en faveur de la réalisation d'une formule sanguine complète un jour à l'avance. Seulement 24% des personnes interrogées estimaient que la prescription de tests sanguins à l'avance entraînait fréquemment des changements dans la prise en charge. Les facteurs les plus souvent perçus comme influençant la prescription d'analyses sanguines à l'unité de soins intensifs étaient les préférences des médecins, les habitudes institutionnelles et les ensembles d'ordonnances. CONCLUSION: La plupart des répondant·es à ce sondage ont l'impression que des tests sanguins inutiles sont prescrits aux soins intensifs. L'enquête a permis d'identifier des stratégies possibles pour réduire le nombre de tests sanguins.

Understanding vermicompost and organic manure interactions: impact on toxic elements, nitrification activity, comammox Nitrospira inopinata, and archaea/bacteria.

Vermicompost is a substantial source of nutrients, promotes soil fertility, and maintains or increases soil organic matter levels. Potentially toxic elements (PTEs) in vermicompost impact on nitrification activity. However, it is yet unknown how vermicompost affects nitrifying bacteria and archaea, comammox Nitrospira inopinata (complete ammonia oxidizers), net nitrification rates (NNRs), and PTEs. The effects of vermicompost application on NNRs, potential nitrification rates (NPs), PTEs, and the abundances of comammox N. inopinata bacteria, nitrite-oxidizing bacteria (NOB), and ammonia-oxidizing bacteria (AOB)/archaea (AOA) were studied. NNRs and NPs were significantly higher (p < 0.05) in fresh cow-dung vermicompost (stored for 40 days) as compared with other organic manure. The level of PTEs (Cu2+, Fe2+, Pb2+, Cd2+, and Zn2+) was significantly lower (p < 0.05) in vermicompost as compared with compost of waste material with Trichoderma and cow dung. Comammox N. inopinata, NOB, AOB, and AOA were significantly higher (p < 0.05) in stored cow-dung vermicompost (more than 1 year) as compared with other organic manure. The results of the scatterplot matrix analysis suggested that Fe2+, total nitrogen (TN), soil organic carbon (SOC), and total carbon (TC) were linearly correlated (p < 0.001) with NNRs and NPs in vermicompost and organic manure. Similarly, comammox N. inopinata bacteria, NOB, AOB, and AOA were linearly correlated (p < 0.001) with NNR and NP. These results indicated that vermicompost promoted nitrification activity by increasing microbial diversity and abundance, supplying nutrients and organic matter for microbial growth, and facilitating complex microbial interactions. It may be concluded that the influence of vermicompost, which played a great role in PTE concentration reduction, increased chemical, and biological properties, increased the growth rate of nitrifying bacteria/archaea and the nitrogen cycle.

Genetically encoded Runx3 and CD4+ intestinal epithelial lymphocyte deficiencies link SKG mouse and human predisposition to spondyloarthropathy.

Disturbances in immune regulation, intestinal dysbiosis and inflammation characterize ankylosing spondylitis (AS), which is associated with RUNX3 loss-of-function variants. ZAP70W163C mutant (SKG) mice have reduced ZAP70 signaling, spondyloarthritis and ileitis. In small intestine, Foxp3+ regulatory T cells (Treg) and CD4+CD8αα+TCRαß+ intraepithelial lymphocytes (CD4-IEL) control inflammation. TGF-ß and retinoic acid (RA)-producing dendritic cells and MHC-class II+ intestinal epithelial cells (IEC) are required for Treg and CD4-IEL differentiation from CD4+ conventional or Treg precursors, with upregulation of Runx3 and suppression of ThPOK. We show in SKG mouse ileum, that ZAP70W163C or ZAP70 inhibition prevented CD4-IEL but not Treg differentiation, dysregulating Runx3 and ThPOK. TGF-ß/RA-mediated CD4-IEL development, T-cell IFN-γ production, MHC class-II+ IEC, tissue-resident memory T-cell and Runx3-regulated genes were reduced. In AS intestine, CD4-IEL were decreased, while in AS blood CD4+CD8+ T cells were reduced and Treg increased. Thus, genetically-encoded TCR signaling dysfunction links intestinal T-cell immunodeficiency in mouse and human spondyloarthropathy.

Duodenal Microbiota in Stunted Undernourished Children with Enteropathy.

BACKGROUND: Environmental enteric dysfunction (EED) is an enigmatic disorder of the small intestine that is postulated to play a role in childhood undernutrition, a pressing global health problem. Defining the incidence of this disorder, its pathophysiological features, and its contribution to impaired linear and ponderal growth has been hampered by the difficulty in directly sampling the small intestinal mucosa and microbial community (microbiota). METHODS: In this study, among 110 young children (mean age, 18 months) with linear growth stunting who were living in an urban slum in Dhaka, Bangladesh, and had not benefited from a nutritional intervention, we performed endoscopy in 80 children who had biopsy-confirmed EED and available plasma and duodenal samples. We quantified the levels of 4077 plasma proteins and 2619 proteins in duodenal biopsy samples obtained from these children. The levels of bacterial strains in microbiota recovered from duodenal aspirate from each child were determined with the use of culture-independent methods. In addition, we obtained 21 plasma samples and 27 fecal samples from age-matched healthy children living in the same area. Young germ-free mice that had been fed a Bangladeshi diet were colonized with bacterial strains cultured from the duodenal aspirates. RESULTS: Of the bacterial strains that were obtained from the children, the absolute levels of a shared group of 14 taxa (which are not typically classified as enteropathogens) were negatively correlated with linear growth (length-for-age z score, r = -0.49; P = 0.003) and positively correlated with duodenal proteins involved in immunoinflammatory responses. The representation of these 14 duodenal taxa in fecal microbiota was significantly different from that in samples obtained from healthy children (P<0.001 by permutational multivariate analysis of variance). Enteropathy of the small intestine developed in gnotobiotic mice that had been colonized with cultured duodenal strains obtained from children with EED. CONCLUSIONS: These results provide support for a causal relationship between growth stunting and components of the small intestinal microbiota and enteropathy and offer a rationale for developing therapies that target these microbial contributions to EED. (Funded by the Bill and Melinda Gates Foundation and others; ClinicalTrials.gov number, NCT02812615.).