Total RNA sequencing reveals gene expression and microbial alterations shared by oral pre-malignant lesions and cancer.

Head and neck cancers are a complex malignancy comprising multiple anatomical sites, with cancer of the oral cavity ranking among the deadliest and the most disfiguring cancers globally. Oral cancer (OC) constitutes a subset of head and neck cancer cases, presenting primarily as tobacco- and alcohol-associated oral squamous cell carcinoma (OSCC), with a 5-year survival rate of ~ 65%, partly due to the lack of early detection and effective treatments. OSCC arises from premalignant lesions (PMLs) in the oral cavity through a multi-step series of clinical and histopathological stages, including varying degrees of epithelial dysplasia. To gain insights into the molecular mechanisms associated with the progression of PMLs to OSCC, we profiled the whole transcriptome of 66 human PMLs comprising leukoplakia with dysplasia and hyperkeratosis non-reactive (HkNR) pathologies, alongside healthy controls and OSCC. Our data revealed that PMLs were enriched in gene signatures associated with cellular plasticity, such as partial EMT (p-EMT) phenotypes, and with immune response. Integrated analyses of the host transcriptome and microbiome further highlighted a significant association between differential microbial abundance and PML pathway activity, suggesting a contribution of the oral microbiome toward PML evolution to OSCC. Collectively, this study reveals molecular processes associated with PML progression that may help early diagnosis and disease interception at an early stage.

ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes.

MOTIVATION: The detection of distinct cellular identities is central to the analysis of single-cell RNA sequencing (scRNA-seq) experiments. However, in perturbation experiments, current methods typically fail to correctly match cell states between conditions or erroneously remove population substructure. Here, we present the novel, unsupervised algorithm Identify Cell states Across Treatments (ICAT) that employs self-supervised feature weighting and control-guided clustering to accurately resolve cell states across heterogeneous conditions. RESULTS: Using simulated and real datasets, we show ICAT is superior in identifying and resolving cell states compared with current integration workflows. While requiring no a priori knowledge of extant cell states or discriminatory marker genes, ICAT is robust to low signal strength, high perturbation severity, and disparate cell type proportions. We empirically validate ICAT in a developmental model and find that only ICAT identifies a perturbation-unique cellular response. Taken together, our results demonstrate that ICAT offers a significant improvement in defining cellular responses to perturbation in scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: https://github.com/BradhamLab/icat.

Total RNA sequencing reveals gene expression and microbial alterations shared by oral pre-malignant lesions and cancer.

Head and neck cancers are a complex malignancy comprising multiple anatomical sites, with cancer of the oral cavity ranking among the deadliest and most disfiguring cancers globally. Oral cancer (OC) constitutes a subset of head and neck cancer cases, presenting primarily as tobacco-and alcohol-associated oral squamous cell carcinoma (OSCC), with a 5-year survival rate of ∻65%, partly due to the lack of early detection and effective treatments. OSCC arises from premalignant lesions (PMLs) in the oral cavity through a multi-step series of clinical and histopathological stages, including varying degrees of epithelial dysplasia. To gain insights into the molecular mechanisms associated with the progression of PMLs to OSCC, we profiled the whole transcriptome of 66 human PMLs comprising leukoplakia with dysplasia and hyperkeratosis non-reactive (HkNR) pathologies, alongside healthy controls and OSCC. Our data revealed that PMLs were enriched in gene signatures associated with cellular plasticity, such as partial EMT (p-EMT) phenotypes, and with immune response. Integrated analyses of the host transcriptome and microbiome further highlighted a significant association between differential microbial abundance and PML pathway activity, suggesting a contribution of the oral microbiome towards PML evolution to OSCC. Collectively, this study reveals molecular processes associated with PML progression that may help early diagnosis and disease interception at an early stage. AUTHOR SUMMARY: Patients harboring oral premalignant lesions (PMLs) have an increased risk of developing oral squamous cell carcinoma (OSCC), but the underlying mechanisms driving transformation of PMLs to OSCC remain poorly understood. In this study, Khan et al., analyzed a newly generated dataset of gene expression and microbial profiles of oral tissues from patients diagnosed with PMLs from differing histopathological groups, including hyperkeratosis not reactive ( HkNR ) and dysplasia, comparing these profiles with OSCC and normal oral mucosa. Significant similarities between PMLs and OSCC were observed, with PMLs manifesting several cancer hallmarks, including oncogenic and immune pathways. The study also demonstrates associations between the abundance of multiple microbial species and PML groups, suggesting a potential contribution of the oral microbiome to the early stages of OSCC development. The study offers insights into the nature of the molecular, cellular and microbial heterogeneity of oral PMLs and suggests that molecular and clinical refinement of PMLs may provide opportunities for early disease detection and interception.

Corrigendum: Malnutrition leads to increased inflammation and expression of tuberculosis risk signatures in recently exposed household contacts of pulmonary tuberculosis.

[This corrects the article DOI: 10.3389/fimmu.2022.1011166.].

Prevalence and risk factors associated with latent tuberculosis infection among household contacts of smear positive pulmonary tuberculosis patients in South India.

OBJECTIVE: We aimed to determine the prevalence and find the risk factors associated with latent tuberculosis infection (LTBI) among the household contacts (HHC) of pulmonary TB patients. METHODS: This cohort study was conducted from 2014 to 2019. Pretested standardised questionnaires and tools were used for data collection. The prevalence of LTBI among HHCs of TB patients was summarised as proportion with 95% confidence interval (CI). Mixed-effects generalised linear modelling function (meglm) in STATA with family Poisson and log link was performed to find the factors associated with LTBI. RESULTS: In total, 1523 HHC of pulmonary TB patients were included in the study. Almost all HHC shared their residence with the index case (IC) for more than a year; 25% shared the same bed with the IC. The prevalence of LTBI among the HHC of TB patients was 52.6% (95% CI: 50.1-55.1%). In an adjusted model, we found that among HHC belonging to the age group of 19-64 years (aIRR = 1.2; 95% CI: 1.1-1.3; p-value: 0.02), to the age group >65 years (aIRR = 1.4, 95% CI: 1.1-1.9, p-value: 0.02) and sharing the same bed with the IC (aIRR = 1.2, 95% CI: 1.1-1.3, p value: 0.04) were independent determinants of LTBI among the HHC. CONCLUSION: One in two household contacts of TB patients have latent tuberculosis infection. This underscores the need of targeted contact screening strategies, effective contact tracing and testing using standardised methods in high TB burden settings.

Sex Differences in Behavioral and Brainstem Transcriptomic Neuroadaptations following Neonatal Opioid Exposure in Outbred Mice.

The opioid epidemic led to an increase in the number of neonatal opioid withdrawal syndrome (NOWS) cases in infants born to opioid-dependent mothers. Hallmark features of NOWS include weight loss, severe irritability, respiratory problems, and sleep fragmentation. Mouse models provide an opportunity to identify brain mechanisms that contribute to NOWS. Neonatal outbred Swiss Webster Cartworth Farms White (CFW) mice were administered morphine (15 mg/kg, s.c.) twice daily from postnatal day 1 (P1) to P14, an approximation of the third trimester of human gestation. Female and male mice underwent behavioral testing on P7 and P14 to determine the impact of opioid exposure on anxiety and pain sensitivity. Ultrasonic vocalizations (USVs) and daily body weights were also recorded. Brainstems containing pons and medulla were collected during morphine withdrawal on P14 for RNA sequencing. Morphine induced weight loss from P2 to P14, which persisted during adolescence (P21) and adulthood (P50). USVs markedly increased at P7 in females, emerging earlier than males. On P7 and P14, both morphine-exposed female and male mice displayed hyperalgesia on the hot plate and tail-flick assays, with females showing greater hyperalgesia than males. Morphine-exposed mice exhibited increased anxiety-like behavior in the open-field arena on P21. Transcriptome analysis of the brainstem, an area implicated in opioid withdrawal and NOWS, identified pathways enriched for noradrenergic signaling in females and males. We also found sex-specific pathways related to mitochondrial function and neurodevelopment in females and circadian entrainment in males. Sex-specific transcriptomic neuroadaptations implicate unique neurobiological mechanisms underlying NOWS-like behaviors.

Non-surgical oral hygiene interventions on disease activity of Rheumatoid arthritis patients with periodontitis: A randomized controlled trial.

Background. Periodontitis and rheumatoid arthritis have similar epidemiology and pathophysiology. Understanding the interaction between these two diseases is vital in our settings. We set out to assess the effect of oral hygiene interventions on disease activity of rheumatoid arthritis patients with periodontitis in Kampala, Uganda. Methods. Fifty-eight patients attending an arthritis clinic with rheumatoid arthritis and periodontitis were randomly assigned to either an intervention group or a control group. Patients diagnosed with rheumatoid arthritis at least two years before, who were on the same medication, dose, or formulation for RA treatment during the preceding three months, were included. The patients were >18 years of age, would be available for all the study visits in the next six months, had at least six natural teeth, had periodontal disease classified as Dutch Periodontal Index (DPSI) >3 and provided written informed consent. Those who had a chronic disorder requiring chronic or intermittent use of antibiotics, were pregnant, were lactating, or had intent to become pregnant were excluded. The primary outcome measure was a change in Disease Activity Score of 28 Joints (DAS28 score) in two 3-month follow-up periods after the intervention. The secondary outcome measure was a change in periodontal status. Results. There was a statistically significant improvement in the DAS-28 score in both the intervention and control arms during the follow-up period (P<0.01). The participants carrying more than one bacterial species had worse DAS-28 scores. Conclusion. Oral hygiene interventions given to RA patients could drastically improve their RA treatment outcomes, especially in resource-limited settings.

Periodontitis and Rheumatoid Arthritis in sub-Saharan Africa, gaps and way forward: a systematic review and meta-analysis.

BACKGROUND: This review identified papers that described periodontitis and rheumatoid arthritis in sub-Saharan Africa. Only English language publications from January 2010 to December 2017 describing original research in sub-Saharan Africa on the association between periodontitis and rheumatoid arthritis were considered for this study. METHODS: Published databases: PubMed, Science direct and Google scholar, were searched using terms "periodontitis", "rheumatoid arthritis" and "Sub-Saharan Africa" to generate a set of putative studies. Articles with data on both rheumatoid arthritis and periodontitis compared to controls were selected. Studies on the association of periodontitis with cardiovascular disease, arthritis or rheumatoid arthritis alone were excluded. Data were extracted, critically appraised, and analyzed using a random-effect Mantel-Haenszel meta-analysis on plaque index, gingival index, pocket depth and clinical attachment loss. RESULTS: Three publications were selected for the systematic review and 2 for the meta-analysis. Two studies were from Sudan, and one was from Burina Faso. There was a significant increase in pocket depth (mean difference: 0.31; 95% CI: 0.21, 0.41; N= 274; (p ≤ 0.001) and clinical attachment loss (mean difference: 0.47; 95% CI: 0.22, 0.75; N= 274; (p ≤ 0.001) in participants with rheumatoid arthritis compared to normal controls. CONCLUSION: Findings from these combined studies show a significant relationship between periodontal disease and rheumatoid arthritis with increased periodontal pocket depth and clinical attachment loss. They also highlight the need for additional work especially in the area of associating rheumatoid arthritis with P. gingivalis, the oral microbiome and treating periodontal diseases to help in the management of rheumatoid arthritis.

Detection of multiple perturbations in multi-omics biological networks.

Cellular mechanism-of-action is of fundamental concern in many biological studies. It is of particular interest for identifying the cause of disease and learning the way in which treatments act against disease. However, pinpointing such mechanisms is difficult, due to the fact that small perturbations to the cell can have wide-ranging downstream effects. Given a snapshot of cellular activity, it can be challenging to tell where a disturbance originated. The presence of an ever-greater variety of high-throughput biological data offers an opportunity to examine cellular behavior from multiple angles, but also presents the statistical challenge of how to effectively analyze data from multiple sources. In this setting, we propose a method for mechanism-of-action inference by extending network filtering to multi-attribute data. We first estimate a joint Gaussian graphical model across multiple data types using penalized regression and filter for network effects. We then apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. In addition, we propose a conditional testing procedure to allow for detection of multiple perturbations. We demonstrate this methodology on paired gene expression and methylation data from The Cancer Genome Atlas (TCGA).

Integrative analyses reveal signaling pathways underlying familial breast cancer susceptibility.

The signaling events that drive familial breast cancer (FBC) risk remain poorly understood. While the majority of genomic studies have focused on genetic risk variants, known risk variants account for at most 30% of FBC cases. Considering that multiple genes may influence FBC risk, we hypothesized that a pathway-based strategy examining different data types from multiple tissues could elucidate the biological basis for FBC. In this study, we performed integrated analyses of gene expression and exome-sequencing data from peripheral blood mononuclear cells and showed that cell adhesion pathways are significantly and consistently dysregulated in women who develop FBC. The dysregulation of cell adhesion pathways in high-risk women was also identified by pathway-based profiling applied to normal breast tissue data from two independent cohorts. The results of our genomic analyses were validated in normal primary mammary epithelial cells from high-risk and control women, using cell-based functional assays, drug-response assays, fluorescence microscopy, and Western blotting assays. Both genomic and cell-based experiments indicate that cell-cell and cell-extracellular matrix adhesion processes seem to be disrupted in non-malignant cells of women at high risk for FBC and suggest a potential role for these processes in FBC development.

PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples.

BACKGROUND: Recent innovations in sequencing technologies have provided researchers with the ability to rapidly characterize the microbial content of an environmental or clinical sample with unprecedented resolution. These approaches are producing a wealth of information that is providing novel insights into the microbial ecology of the environment and human health. However, these sequencing-based approaches produce large and complex datasets that require efficient and sensitive computational analysis workflows. Many recent tools for analyzing metagenomic-sequencing data have emerged, however, these approaches often suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework. RESULTS: We present PathoScope 2.0, a complete bioinformatics framework for rapidly and accurately quantifying the proportions of reads from individual microbial strains present in metagenomic sequencing data from environmental or clinical samples. The pipeline performs all necessary computational analysis steps; including reference genome library extraction and indexing, read quality control and alignment, strain identification, and summarization and annotation of results. We rigorously evaluated PathoScope 2.0 using simulated data and data from the 2011 outbreak of Shiga-toxigenic Escherichia coli O104:H4. CONCLUSIONS: The results show that PathoScope 2.0 is a complete, highly sensitive, and efficient approach for metagenomic analysis that outperforms alternative approaches in scope, speed, and accuracy. The PathoScope 2.0 pipeline software is freely available for download at: http://sourceforge.net/projects/pathoscope/.

Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data.

BACKGROUND: The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens. RESULTS: Here we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity. CONCLUSIONS: Clinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at: http://sourceforge.net/projects/pathoscope/.

PathoQC: Computationally Efficient Read Preprocessing and Quality Control for High-Throughput Sequencing Data Sets.

Quality control and read preprocessing are critical steps in the analysis of data sets generated from high-throughput genomic screens. In the most extreme cases, improper preprocessing can negatively affect downstream analyses and may lead to incorrect biological conclusions. Here, we present PathoQC, a streamlined toolkit that seamlessly combines the benefits of several popular quality control software approaches for preprocessing next-generation sequencing data. PathoQC provides a variety of quality control options appropriate for most high-throughput sequencing applications. PathoQC is primarily developed as a module in the PathoScope software suite for metagenomic analysis. However, PathoQC is also available as an open-source Python module that can run as a stand-alone application or can be easily integrated into any bioinformatics workflow. PathoQC achieves high performance by supporting parallel computation and is an effective tool that removes technical sequencing artifacts and facilitates robust downstream analysis. The PathoQC software package is available at http://sourceforge.net/projects/PathoScope/.

Probabilistic alignment leads to improved accuracy and read coverage for bisulfite sequencing data.

BACKGROUND: DNA methylation has been linked to many important biological phenomena. Researchers have recently begun to sequence bisulfite treated DNA to determine its pattern of methylation. However, sequencing reads from bisulfite-converted DNA can vary significantly from the reference genome because of incomplete bisulfite conversion, genome variation, sequencing errors, and poor quality bases. Therefore, it is often difficult to align reads to the correct locations in the reference genome. Furthermore, bisulfite sequencing experiments have the additional complexity of having to estimate the DNA methylation levels within the sample. RESULTS: Here, we present a highly accurate probabilistic algorithm, which is an extension of the Genomic Next-generation Universal MAPper to accommodate bisulfite sequencing data (GNUMAP-bs), that addresses the computational problems associated with aligning bisulfite sequencing data to a reference genome. GNUMAP-bs integrates uncertainty from read and mapping qualities to help resolve the difference between poor quality bases and the ambiguity inherent in bisulfite conversion. We tested GNUMAP-bs and other commonly-used bisulfite alignment methods using both simulated and real bisulfite reads and found that GNUMAP-bs and other dynamic programming methods were more accurate than the more heuristic methods. CONCLUSIONS: The GNUMAP-bs aligner is a highly accurate alignment approach for processing the data from bisulfite sequencing experiments. The GNUMAP-bs algorithm is freely available for download at: http://dna.cs.byu.edu/gnumap. The software runs on multiple threads and multiple processors to increase the alignment speed.

Exome sequencing and unrelated findings in the context of complex disease research: ethical and clinical implications.

Exome sequencing has identified the causes of several Mendelian diseases, although it has rarely been used in a clinical setting to diagnose the genetic cause of an idiopathic disorder in a single patient. We performed exome sequencing on a pedigree with several members affected with attention deficit/hyperactivity disorder (ADHD), in an effort to identify candidate variants predisposing to this complex disease. While we did identify some rare variants that might predispose to ADHD, we have not yet proven the causality for any of them. However, over the course of the study, one subject was discovered to have idiopathic hemolytic anemia (IHA), which was suspected to be genetic in origin. Analysis of this subject's exome readily identified two rare non-synonymous mutations in PKLR gene as the most likely cause of the IHA, although these two mutations had not been documented before in a single individual. We further confirmed the deficiency by functional biochemical testing, consistent with a diagnosis of red blood cell pyruvate kinase deficiency. Our study implies that exome and genome sequencing will certainly reveal additional rare variation causative for even well-studied classical Mendelian diseases, while also revealing variants that might play a role in complex diseases. Furthermore, our study has clinical and ethical implications for exome and genome sequencing in a research setting; how to handle unrelated findings of clinical significance, in the context of originally planned complex disease research, remains a largely uncharted area for clinicians and researchers.