Communicating computational workflows in a regulatory environment

The volume of nucleic acid sequence data has exploded recently, amplifying the challenge of transforming data into meaningful information. Processing data can require an increasingly complex ecosystem of customized tools, which increases difficulty in communicating analyses in an understandable way yet is of sufficient detail to enable informed decisions or repeats. This can be of particular interest to institutions and companies communicating computations in a regulatory environment. BioCompute Objects (BCOs; an instance of pipeline documentation that conforms to the IEEE 2791-2020 standard) were developed as a standardized mechanism for analysis reporting. A suite of BCOs is presented, representing interconnected elements of a computation modeled after those that might be found in a regulatory submission but are shared publicly - in this case a pipeline designed to identify viral contaminants in biological manufacturing, such as for vaccines.

Vaccine Hyporesponse Induced by Individual Antibiotic Treatment in Mice and Non-Human Primates Is Diminished upon Recovery of the Gut Microbiome

Emerging evidence demonstrates a connection between microbiome composition and suboptimal response to vaccines (vaccine hyporesponse). Harnessing the interaction between microbes and the immune system could provide novel therapeutic strategies for improving vaccine response. Currently we do not fully understand the mechanisms and dynamics by which the microbiome influences vaccine response. Using both mouse and non-human primate models, we report that short-term oral treatment with a single antibiotic (vancomycin) results in the disruption of the gut microbiome and this correlates with a decrease in systemic levels of antigen-specific IgG upon subsequent parenteral vaccination. We further show that recovery of microbial diversity before vaccination prevents antibiotic-induced vaccine hyporesponse, and that the antigen specific IgG response correlates with the recovery of microbiome diversity. RNA sequencing analysis of small intestine, spleen, whole blood, and secondary lymphoid organs from antibiotic treated mice revealed a dramatic impact on the immune system, and a muted inflammatory signature is correlated with loss of bacteria from Lachnospiraceae, Ruminococcaceae, and Clostridiaceae. These results suggest that microbially modulated immune pathways may be leveraged to promote vaccine response and will inform future vaccine design and development strategies.

The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability

Covering: up to the end of 2020. The machine learning field can be defined as the study and application of algorithms that perform classification and prediction tasks through pattern recognition instead of explicitly defined rules. Among other areas, machine learning has excelled in natural language processing. As such methods have excelled at understanding written languages (e.g. English), they are also being applied to biological problems to better understand the "genomic language". In this review we focus on recent advances in applying machine learning to natural products and genomics, and how those advances are improving our understanding of natural product biology, chemistry, and drug discovery. We discuss machine learning applications in genome mining (identifying biosynthetic signatures in genomic data), predictions of what structures will be created from those genomic signatures, and the types of activity we might expect from those molecules. We further explore the application of these approaches to data derived from complex microbiomes, with a focus on the human microbiome. We also review challenges in leveraging machine learning approaches in the field, and how the availability of other "omics" data layers provides value. Finally, we provide insights into the challenges associated with interpreting machine learning models and the underlying biology and promises of applying machine learning to natural product drug discovery. We believe that the application of machine learning methods to natural product research is poised to accelerate the identification of new molecular entities that may be used to treat a variety of disease indications.

A deep learning genome-mining strategy for biosynthetic gene cluster prediction

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing machine-learning tools. We supplemented this with random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable putative BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a major addition to in-silico BGC identification.

The microbiota of traumatic, open fracture wounds is associated with mechanism of injury

Open fractures are characterized by disruption of the skin and soft tissue, which allows for microbial contamination and colonization. Preventing infection-related complications of open fractures and other acute wounds remains an evolving challenge due to an incomplete understanding of how microbial colonization and contamination influence healing and outcomes. Culture-independent molecular methods are now widely used to study human-associated microbial communities without introducing culture biases. Using such approaches, the objectives of this study were to (1) define the long-term temporal microbial community dynamics of open fracture wounds and (2) examine microbial community dynamics with respect to clinical and demographic factors. Fifty-two subjects with traumatic open fracture wounds (32 blunt and 20 penetrating injuries) were enrolled prospectively and sampled longitudinally from presentation to the emergency department (ED) and at each subsequent inpatient or outpatient encounter. Specimens were collected from both the wound center and adjacent skin. Culture-independent sequencing of the 16S ribosomal RNA gene was employed to identify and characterize microbiota. Upon presentation to the ED and time points immediately following, sample collection site (wound or adjacent skin) was the most defining feature discriminating microbial profiles. Microbial composition of adjacent skin and wound center converged over time. Mechanism of injury most strongly defined the microbiota after initial convergence. Further analysis controlling for race, gender, and age revealed that mechanism of injury remained a significant discriminating feature throughout the continuum of care. We conclude that the microbial communities associated with open fracture wounds are dynamic in nature until eventual convergence with the adjacent skin community during healing, with mechanism of injury as an important feature affecting both diversity and composition of the microbiota. A more complete understanding of the factors influencing microbial contamination and/or colonization in open fractures is a critical foundation for identifying markers indicative of outcome and deciphering their respective contributions to healing and/or complication.

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks.

The human microbiome, the collection of microbial communities that colonize the human body, is a crucial component to health and disease. Two major components of the human microbiome are the bacterial and viral communities. These communities have primarily been studied separately using metrics of community composition and diversity. These approaches have failed to capture the complex dynamics of interacting bacteria and phage communities, which frequently share genetic information and work together to maintain ecosystem homestatsis (e.g. kill-the-winner dynamics). Removal of bacteria or phage can disrupt or even collapse those ecosystems. Relationship-based network approaches allow us to capture this interaction information. Using this network-based approach with three independent human cohorts, we were able to present an initial understanding of how phage-bacteria networks differ throughout the human body, so as to provide a baseline for future studies of how and why microbiome networks differ in disease states.

Evolutionary and functional implications of hypervariable loci within the skin virome

Localized genomic variability is crucial for the ongoing conflicts between infectious microbes and their hosts. An understanding of evolutionary and adaptive patterns associated with genomic variability will help guide development of vaccines and antimicrobial agents. While most analyses of the human microbiome have focused on taxonomic classification and gene annotation, we investigated genomic variation of skin-associated viral communities. We evaluated patterns of viral genomic variation across 16 healthy human volunteers. Human papillomavirus (HPV) and Staphylococcus phages contained 106 and 465 regions of diversification, or hypervariable loci, respectively. Propionibacterium phage genomes were minimally divergent and contained no hypervariable loci. Genes containing hypervariable loci were involved in functions including host tropism and immune evasion. HPV and Staphylococcus phage hypervariable loci were associated with purifying selection. Amino acid substitution patterns were virus dependent, as were predictions of their phenotypic effects. We identified diversity generating retroelements as one likely mechanism driving hypervariability. We validated these findings in an independently collected skin metagenomic sequence dataset, suggesting that these features of skin virome genomic variability are widespread. Our results highlight the genomic variation landscape of the skin virome and provide a foundation for better understanding community viral evolution and the functional implications of genomic diversification of skin viruses.

Skin Microbiome Surveys Are Strongly Influenced by Experimental Design

Culture-independent studies to characterize skin microbiota are increasingly common, due in part to affordable and accessible sequencing and analysis platforms. Compared to culture-based techniques, DNA sequencing of the bacterial 16S ribosomal RNA (rRNA) gene or whole metagenome shotgun (WMS) sequencing provides more precise microbial community characterizations. Most widely used protocols were developed to characterize microbiota of other habitats (i.e., gastrointestinal) and have not been systematically compared for their utility in skin microbiome surveys. Here we establish a resource for the cutaneous research community to guide experimental design in characterizing skin microbiota. We compare two widely sequenced regions of the 16S rRNA gene to WMS sequencing for recapitulating skin microbiome community composition, diversity, and genetic functional enrichment. We show that WMS sequencing most accurately recapitulates microbial communities, but sequencing of hypervariable regions 1-3 of the 16S rRNA gene provides highly similar results. Sequencing of hypervariable region 4 poorly captures skin commensal microbiota, especially Propionibacterium. WMS sequencing, which is resource and cost intensive, provides evidence of a community's functional potential; however, metagenome predictions based on 16S rRNA sequence tags closely approximate WMS genetic functional profiles. This study highlights the importance of experimental design for downstream results in skin microbiome surveys.

The human skin double-stranded DNA virome: topographical and temporal diversity, genetic enrichment, and dynamic associations with the host microbiome

Viruses make up a major component of the human microbiota but are poorly understood in the skin, our primary barrier to the external environment. Viral communities have the potential to modulate states of cutaneous health and disease. Bacteriophages are known to influence the structure and function of microbial communities through predation and genetic exchange. Human viruses are associated with skin cancers and a multitude of cutaneous manifestations. Despite these important roles, little is known regarding the human skin virome and its interactions with the host microbiome. Here we evaluated the human cutaneous double-stranded DNA virome by metagenomic sequencing of DNA from purified virus-like particles (VLPs). In parallel, we employed metagenomic sequencing of the total skin microbiome to assess covariation and infer interactions with the virome. Samples were collected from 16 subjects at eight body sites over 1 month. In addition to the microenviroment, which is known to partition the bacterial and fungal microbiota, natural skin occlusion was strongly associated with skin virome community composition. Viral contigs were enriched for genes indicative of a temperate phage replication style and also maintained genes encoding potential antibiotic resistance and virulence factors. CRISPR spacers identified in the bacterial DNA sequences provided a record of phage predation and suggest a mechanism to explain spatial partitioning of skin phage communities. Finally, we modeled the structure of bacterial and phage communities together to reveal a complex microbial environment with a Corynebacterium hub. These results reveal the previously underappreciated diversity, encoded functions, and viral-microbial dynamic unique to the human skin virome.

IMPORTANCE

To date, most cutaneous microbiome studies have focused on bacterial and fungal communities. Skin viral communities and their relationships with their hosts remain poorly understood despite their potential to modulate states of cutaneous health and disease. Previous studies employing whole-metagenome sequencing without purification for virus-like particles (VLPs) have provided some insight into the viral component of the skin microbiome but have not completely characterized these communities or analyzed interactions with the host microbiome. Here we present an optimized virus purification technique and corresponding analysis tools for gaining novel insights into the skin virome, including viral "dark matter," and its potential interactions with the host microbiome. The work presented here establishes a baseline of the healthy human skin virome and is a necessary foundation for future studies examining viral perturbations in skin health and disease.

Current Concepts and Ongoing Research in the Prevention and Treatment of Open Fracture Infections

Significance: Open fractures are fractures in which the bone has violated the skin and soft tissue. Because of their severity, open fractures are associated with complications that can result in increased lengths of hospital stays, multiple operative interventions, and even amputation. One of the factors thought to influence the extent of these complications is exposure and contamination of the open fracture with environmental microorganisms, potentially those that are pathogenic in nature. Recent Advances: Current open fracture care aims to prevent infection by wound classification, prophylactic antibiotic administration, debridement and irrigation, and stable fracture fixation. Critical Issues: Despite these established treatment paradigms, infections and infection-related complications remain a significant clinical burden. To address this, improvements need to be made in our ability to detect bacterial infections, effectively remove wound contamination, eradicate infections, and treat and prevent biofilm formation associated with fracture fixation hardware. Future Directions: Current research is addressing these critical issues. While culture methods are of limited value, culture-independent molecular techniques are being developed to provide informative detection of bacterial contamination and infection. Other advanced contamination- and infection-detecting techniques are also being investigated. New hardware-coating methods are being developed to minimize the risk of biofilm formation in wounds, and immune stimulation techniques are being developed to prevent open fracture infections.

Culture-independent pilot study of microbiota colonizing open fractures and association with severity, mechanism, location, and complication from presentation to early outpatient follow-up

Precise identification of bacteria associated with post-injury infection, co-morbidities, and outcomes could have a tremendous impact in the management and treatment of open fractures. We characterized microbiota colonizing open fractures using culture-independent, high-throughput DNA sequencing of bacterial 16S ribosomal RNA genes, and analyzed those communities with respect to injury mechanism, severity, anatomical site, and infectious complications. Thirty subjects presenting to the Hospital of the University of Pennsylvania for acute care of open fractures were enrolled in a prospective cohort study. Microbiota was collected from wound center and adjacent skin upon presentation to the emergency department, intraoperatively, and at two outpatient follow-up visits at approximately 25 and 50 days following initial presentation. Bacterial community composition and diversity colonizing open fracture wounds became increasingly similar to adjacent skin microbiota with healing. Mechanism of injury, severity, complication, and location were all associated with various aspects of microbiota diversity and composition. The results of this pilot study demonstrate the diversity and dynamism of the open fracture microbiota, and their relationship to clinical variables. Validation of these preliminary findings in larger cohorts may lead to the identification of microbiome-based biomarkers of complication risk and/or to aid in management and treatment of open fractures.

Microbial ecology of the skin in the era of metagenomics and molecular microbiology

The skin is the primary physical barrier between the body and the external environment and is also a substrate for the colonization of numerous microbes. Previously, dermatological microbiology research was dominated by culture-based techniques, but significant advances in genomic technologies have enabled the development of less-biased, culture-independent approaches to characterize skin microbial communities. These molecular microbiology approaches illustrate the great diversity of microbiota colonizing the skin and highlight unique features such as site specificity, temporal dynamics, and interpersonal variation. Disruptions in skin commensal microbiota are associated with the progression of many dermatological diseases. A greater understanding of how skin microbes interact with each other and with their host, and how we can therapeutically manipulate those interactions, will provide powerful tools for treating and preventing dermatological disease.

Mycobacterium minnesotense sp. nov., a photochromogenic bacterium isolated from sphagnum peat bogs

Several intermediate-growing, photochromogenic bacteria were isolated from sphagnum peat bogs in northern Minnesota, USA. Acid-fast staining and 16S rRNA gene sequence analysis placed these environmental isolates in the genus Mycobacterium, and colony morphologies and PCR restriction analysis patterns of the isolates were similar. Partial sequences of hsp65 and dnaJ1 from these isolates showed that Mycobacterium arupense ATCC BAA-1242(T) was the closest mycobacterial relative, and common biochemical characteristics and antibiotic susceptibilities existed between the isolates and M. arupense ATCC BAA-1242(T). However, compared to nonchromogenic M. arupense ATCC BAA-1242(T), the environmental isolates were photochromogenic, had a different mycolic acid profile and had reduced cell-surface hydrophobicity in liquid culture. The data reported here support the conclusion that the isolates are representatives of a novel mycobacterial species, for which the name Mycobacterium minnesotense sp. nov. is proposed. The type strain is DL49(T) (=DSM 45633(T) = JCM 17932(T) = NCCB 100399(T)).