Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities.

In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Molecular analysis of the blood meals and bacterial communities of bed bugs (Cimex lectularius L.) to assess interactions with alternative hosts.

Common bed bugs (Cimex lectularius L.) are hematophagous pests present in urban environments across the globe. It is widely established that they have a strong host preference for humans. However, there are records of C. lectularius feeding upon a range of mammalian and avian hosts, including rodents, in the field. There is little information available about how frequently common bed bugs feed on alternative hosts in residential settings, but understanding this phenomenon has implications for both management of infestations and public health. Here, we examined cohorts of C. lectularius collected from 13 different dwellings in the state of New Jersey, USA, that were known to be simultaneously infested with house mice (Mus musculus domesticus). Host-specific quantitative polymerase chain reaction (qPCR) was used to determine if blood meals were taken from mice, while 16S rRNA gene amplicon sequencing was used to screen the bed bugs for the presence of zoonotic bacterial pathogens. We found no evidence that any of the bed bugs we collected fed on mice. Furthermore, the insects harbored depauperate bacterial communities that did not include known human pathogens. However, host-specific qPCR detected feline DNA in a pool of bed bugs from one dwelling, suggesting that interaction with domestic pets should be further investigated. Although sampling in this study was limited, the approach described herein will be useful for additional studies of the interactions between bed bugs and alternative blood meal hosts.

Sub-clustering based recommendation system for stroke patient: Identification of a specific drug class for a given patient.

Stroke is one of the leading causes of death worldwide. Previous studies have explored machine learning techniques for early detection of stroke patients using content-based recommendation systems. However, these models often struggle with timely detection of medications, which can be critical for patient management and decision-making regarding the prescription of new drugs. In this study, we developed a content-based recommendation model using three machine learning algorithms: Gaussian Mixture Model (GMM), Affinity Propagation (AP), and K-Nearest Neighbors (KNN), to aid Healthcare Professionals (HCP) in quickly detecting medications based on the symptoms of a patient with stroke. Our model focused on three classes of drugs: antihypertensive, anticoagulant, and fibrate. Each machine learning algorithm was used to accomplish specific tasks, thereby reducing the partial search space, computational cost, and accurately detecting a primary drug class without loss of precision and accuracy. Our proposed model, called CRGANNC (Clustering Recommendation Gaussian Affinity Nearest Neighbors Classifier), effectively addresses the sparsity and scalability issues faced by content-based recommendation models. The CRGANNC model dynamically partition clusters into sub-clusters with variable numbers based on the group, and can diagnose healthy, sick, and at-risk patients, and recommend drugs to the HCP. In addition to our analysis, we developed a semi-artificial dataset with new features such as weakness, dizziness, headache, nausea, and vomiting, using a pipeline. This dataset serves as a valuable resource for researchers in the sensitive domain of stroke, providing a starting point for building and testing models when real data is often restricted. Our work not only contributes to the development of predictive models for stroke but also establishes a framework for creating similar datasets in other sensitive domains, accelerating research efforts and improving patient care. Our experiments were conducted on our dataset consisting of 9691 patient records, with 1206 records for stroke attacks and 8485 healthy patients. The CRGANNC model achieved an average precision of 0.98, recall of 0.95 and F1-score of 0.96 across all three drugs classes. Furthermore, our model demonstrated significant improvement in computational efficiency compared to existing content-based recommendation models, reducing the processing time by 25.80% . This results indicate the effectiveness of our model in accurately detecting medications for stroke patients based on their symptoms.

Assessment of vector-host-pathogen relationships using data mining and machine learning.

Infectious diseases, including vector-borne diseases transmitted by arthropods, are a leading cause of morbidity and mortality worldwide. In the era of big data, addressing broad-scale, fundamental questions regarding the complex dynamics of these diseases will increasingly require the integration of diverse datasets to produce new biological knowledge. This review provides a current snapshot of the systematic assessment of the relationships between microbial pathogens, arthropod vectors and mammalian hosts using data mining and machine learning. We employ PRISMA to identify 32 key papers relevant to this topic. Our analysis shows an increasing use of data mining and machine learning tasks and techniques, including prediction, classification, clustering, association rules mining, and deep learning, over the last decade. However, it also reveals a number of critical challenges in applying these to the study of vector-host-pathogen interactions at various systems biology levels. Here, relevant studies, current limitations and future directions are discussed. Furthermore, the quality of data in relevant papers was assessed using the FAIR (Findable, Accessible, Interoperable, Reusable) compliance criteria to evaluate and encourage reproducibility and shareability of research outcomes. Although shortcomings in their application remain, data mining and machine learning have significant potential to break new ground in understanding fundamental aspects of vector-host-pathogen relationships and their application in this field should be encouraged. In particular, while predictive modeling, feature engineering and supervised machine learning are already being used in the field, other data mining and machine learning methods such as deep learning and association rules analysis lag behind and should be implemented in combination with established methods to accelerate hypothesis and knowledge generation in the domain.

Body lice and bed bug co-infestation in an emergency department patient, Ohio, USA.

Body lice and bed bugs are hematophagous insects that parasitize humans. Body lice are established vectors of several bacterial pathogens (e.g. Bartonella quintana, Borrelia recurrentis). Bed bugs are biologically competent vectors of some of the same agents, but their vectorial capacity for these in nature is unclear. In particular, a lack of exposure to louse-borne pathogens in bed bugs in the field could be a factor that limits their contribution to transmission. Here, we describe a case of a patient seen in an urban emergency department who was suffering from infestation with both body lice and bed bugs. Insects were collected from the patient and tested for the presence of louse-borne bacterial pathogens using 16S rRNA gene amplicon sequencing. Although no Bartonella, Borrelia, or Rickettsia were detected, this case provides evidence of ecological overlap between body lice and bed bugs and highlights several potential risk factors for co-infestation. The ecological relationships between bed bugs, body lice, and louse-borne bacteria should be further investigated in the field to determine the frequency of co-infestations and identify possible instances of pathogen infection in bed bugs.

Microbiome Differences Between Human Head and Body Lice Ecotypes Revealed by 16S RRNA Gene Amplicon Sequencing.

Human head lice and body lice (Pediculus humanus) are neglected ectoparasites. Head lice continue to be prevalent in children worldwide, and insecticide resistance in these insects has complicated their treatment. Meanwhile, body lice, which are most common in the developing world, are resurging among marginalized populations in developed nations. Today, the microbiome is being increasingly recognized as a key mediator of insect physiology. However, the microbial communities that inhabit human lice have remained unknown beyond only a few species of bacteria. Knowledge of the microbiomes of head and body lice could improve our understanding of the observed physiological differences between the 2 ecotypes and potentially inform the development of novel interventions against lice infestations and louse-borne infectious diseases. Toward these goals, here we performed 16S rRNA gene amplicon sequencing to characterize the microbiomes of both head and body lice and identify patterns of interest among these communities. Our data reveal that head and body lice harbor limited but distinct communities of bacteria that include known intracellular endosymbionts ("Candidatus Riesia pediculicola"), extracellular bacteria that may be horizontally acquired from the host environment, and a number of taxa of known or potential public health significance. Notably, in body lice, the relative abundance of vertically transmitted endosymbionts is lower than in head lice, which is a significant driver of greater alpha diversity. Further, several differentially abundant non-endosymbiont taxa and differences in beta diversity were observed between head lice and body lice. These findings support the hypothesis that microbiome differences could contribute to the divergence between human louse ecotypes and underscore the need for future studies to better comprehend the acquisition and physiological roles of human lice microbiomes.