The Role of Movement Patterns in Epidemic Models on Complex Networks.

In this paper, we analyze the influence of the usual movement variables on the spread of an epidemic. Specifically, given two spatial topologies, we can deduce which topology produces less infected individuals. In particular, we determine the topology that minimizes the overall number of infected individuals. It is worth noting that we do not assume any of the common simplifying assumptions in network theory such as all the links have the same diffusion rate or the movement of the individuals is symmetric. Our main conclusion is that the degree of mobility of the population plays a critical role in the spread of a disease. Finally, we derive theoretical insights to management of epidemics.

Interdisciplinary Approaches to COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has been a significant concern worldwide. The pandemic has demonstrated that public health issues are not merely a health concern but also affect society as a whole. In this chapter, we address the importance of bringing together the world's scientists to find appropriate solutions for controlling and managing the COVID-19 pandemic. Interdisciplinary cooperation, through modern scientific methods, could help to handle the consequences of the pandemic and to avoid the recurrence of future pandemics.

Optimal Network Architectures for Spatially Structured Populations with Heterogeneous Diffusion.

The motivation of this article is to derive new management guidelines to maximize the overall population size using popular management and conservation strategies, such as protected marine areas and ecological corridors. These guidelines are based on the identification of the network architectures for which the total population size is maximized. Describing the biological roles of the typical network variables in the fate of the population is a classic problem with many practical applications. This article suggests that the optimal network architecture relies heavily on the degree of mobility of the population. The recommended network architecture for populations with reduced mobility (in the absence of cost of dispersal and landscapes made up of many sources) is a graph with a patch that has routes toward any other patch with a lower growth rate. However, for highly mobile populations there are many possible network architectures for which the total population size is maximized (e.g., any cyclic graph). We have paid special attention to species with symmetric movement in heterogeneous landscapes. A striking result is that the network architecture does not have any influence on the total population size for highly mobile populations when any pair of different patches can be connected by a sequence of paths.

The urgent need for integrated science to fight COVID-19 pandemic and beyond.

The COVID-19 pandemic has become the leading societal concern. The pandemic has shown that the public health concern is not only a medical problem, but also affects society as a whole; so, it has also become the leading scientific concern. We discuss in this treatise the importance of bringing the world's scientists together to find effective solutions for controlling the pandemic. By applying novel research frameworks, interdisciplinary collaboration promises to manage the pandemic's consequences and prevent recurrences of similar pandemics.

Letrozole treatment of adult female mice results in a similar reproductive phenotype but distinct changes in metabolism and the gut microbiome compared to pubertal mice.

BACKGROUND: A majority of women with polycystic ovary syndrome (PCOS) have metabolic dysfunction that results in an increased risk of type 2 diabetes. We previously developed a pubertal mouse model using the aromatase inhibitor, letrozole, which recapitulates many of the reproductive and metabolic features of PCOS. To further our understanding of the effects of androgen excess, we compared the effects of letrozole treatment initiated in puberty versus adulthood on reproductive and metabolic phenotypes as well as on the gut microbiome. RESULTS: Letrozole treatment of both pubertal and adult female mice resulted in reproductive hallmarks of PCOS, including hyperandrogenemia, anovulation and polycystic ovaries. However, unlike pubertal mice, treatment of adult female mice resulted in modest weight gain and abdominal adiposity, minimal elevation in fasting blood glucose and insulin levels, and no detectable insulin resistance. In addition, letrozole treatment of adult mice was associated with a distinct shift in gut microbial diversity compared to letrozole treatment of pubertal mice. CONCLUSIONS: Our results indicate that dysregulation of metabolism and the gut microbiome in PCOS may be influenced by the timing of androgen exposure. In addition, the minimal weight gain and lack of insulin resistance in adult female mice after letrozole treatment indicates that this model may be useful for investigating the effects of hyperandrogenemia on the hypothalamic-pituitary-gonadal axis and the periphery without the influence of substantial metabolic dysregulation.

Discovery of a Novel Periodontal Disease-Associated Bacterium.

One of the world's most common infectious disease, periodontitis (PD), derives from largely uncharacterized communities of oral bacteria growing as biofilms (a.k.a. plaque) on teeth and gum surfaces in periodontal pockets. Bacteria associated with periodontal disease trigger inflammatory responses in immune cells, which in later stages of the disease cause loss of both soft and hard tissue structures supporting teeth. Thus far, only a handful of bacteria have been characterized as infectious agents of PD. Although deep sequencing technologies, such as whole community shotgun sequencing have the potential to capture a detailed picture of highly complex bacterial communities in any given environment, we still lack major reference genomes for the oral microbiome associated with PD and other diseases. In recent work, by using a combination of supervised machine learning and genome assembly, we identified a genome from a novel member of the Bacteroidetes phylum in periodontal samples. Here, by applying a comparative metagenomics read-classification approach, including 272 metagenomes from various human body sites, and our previously assembled draft genome of the uncultivated Candidatus Bacteroides periocalifornicus (CBP) bacterium, we show CBP's ubiquitous distribution in dental plaque, as well as its strong association with the well-known pathogenic "red complex" that resides in deep periodontal pockets.

Effects of moderate, voluntary ethanol consumption on the rat and human gut microbiome.

Many alcohol-induced health complications are directly attributable to the toxicity of alcohol or its metabolites, but another potential health impact of alcohol may be on the microbial communities of the human gut. Clear distinctions between healthy and diseased-state gut microbiota have been observed in subjects with metabolic diseases, and recent studies suggest that chronic alcoholism is linked to gut microbiome dysbiosis. Here, we investigated the effects of moderate levels of alcohol consumption on the gut microbiome in both rats and humans. The gut microbiota of rats voluntarily consuming a 20 percent ethanol solution, on alternate days, were compared with a non-exposed control group to identify differential taxonomic and functional profiles. Gut microbial diversity profiles were determined using culture-independent amplification, next-generation sequencing and bioinformatic analysis of bacterial 16S ribosomal RNA gene sequence libraries. Our results showed that, compared with controls, ethanol-consuming rats experienced a significant decline in the biodiversity of their gut microbiomes, a state generally associated with dysbiosis. We also observed significant shifts in the overall diversity of the gut microbial communities and a dramatic change in the relative abundance of particular microbes, such as the Lactobacilli. We also compared our results to human fecal microbiome data collected as part of the citizen science American Gut Project. In contrast to the rat data, human drinkers had significantly higher gut microbial biodiversity than non-drinkers. However, we also observed that microbes that differed among the human subjects displayed similar trends in the rat model, including bacteria implicated in metabolic disease.

PARTIE: a partition engine to separate metagenomic and amplicon projects in the Sequence Read Archive.

MOTIVATION: The Sequence Read Archive (SRA) contains raw data from many different types of sequence projects. As of 2017, the SRA contained approximately ten petabases of DNA sequence (10 16 bp). Annotations of the data are provided by the submitter, and mining the data in the SRA is complicated by both the amount of data and the detail within those annotations. Here, we introduce PARTIE, a partition engine optimized to differentiate sequence read data into metagenomic (random) and amplicon (targeted) sequence data sets. RESULTS: PARTIE subsamples reads from the sequencing file and calculates four different statistics: k -mer frequency, 16S abundance, prokaryotic- and viral-read abundance. These metrics are used to create a RandomForest decision tree to classify the sequencing data, and PARTIE provides mechanisms for both supervised and unsupervised classification. We demonstrate the accuracy of PARTIE for classifying SRA data, discuss the probable error rates in the SRA annotations and introduce a resource assessing SRA data. AVAILABILITY AND IMPLEMENTATION: PARTIE and reclassified metagenome SRA entries are available from https://github.com/linsalrob/partie. CONTACT: redwards@mail.sdsu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of diffusion on total biomass in simple metacommunities.

This paper analyzes the effects of diffusion on the overall population size of the different species of a metacommunity. Depending on precise thresholds, we determine whether increasing the dispersal rate of a species has a positive or negative effect on population abundance. These thresholds depend on the interaction type of the species and the quality of the patches. The motivation for researching this issue is that spatial structure is a source of new biological insights with management interest. For instance, in a metacommunity of two competitors, the movement of a competitor could lead to a decrease of the overall population size of both species. On the other hand, we discuss when some classic results of metapopulation theory are preserved in metacommunities. Our results complement some recent experimental work by Zhang and collaborators.

Periodic oscillations of a model for membrane permeability with fluctuating environmental conditions.

We perform an analytical study of the dynamics of a multi-solute model for water transport across a cell membrane under periodic fluctuations of the extracellular solute molalities. Under the presence of non-permeating intracellular solute, water volume experiences periodic oscillations if and only if the extracellular non-permeating solute molality is positive in the average. On the other hand, in the absence of non-permeating intracellular solute, a sufficient condition for the existence of an infinite number of periodic solutions of the model is provided. Such sufficient condition holds automatically in the case of only one permeating solute. The proofs are based on classical tools from the qualitative theory of differential equations, namely Brouwer degree, upper and lower solutions and comparison arguments.

Land use effects on leaf litter breakdown in low-order streams draining a rapidly developing tropical watershed in Puerto Rico.

Land use has an important role influencing stream ecosystem processes, such as leaf litter breakdown. Here, we assessed rates of leaf litter breakdown in low-order tropical streams draining forest, agriculture, and urban land uses in Puerto Rico. To measure leaf breakdown rates, we placed litter bags made of coarse mesh in nine streams, three for each land use type. At each stream, we measured changes in leaf mass over time, leaf breakdown rates, macroinvertebrate assemblages, and stream physicochemistry. Streams differed in their water physicochemistry, with urban streams showing high values for most variables. Stream physical habitat was evaluated using a visual assessment protocol, which indicated that agricultural and urban streams were more degraded than forested streams. Leaf breakdown rates were fast in all streams (k values ranging 0.006-0.024). Breakdown rates were significantly related to the physical conditions of the stream channel (e.g., visual protocol scores), with fastest rates in forested streams. Invertebrates colonizing leaves were mainly mayflies (Leptophlebiidae, Baetidae, and Caenidae), dipterans (Chironomidae), caddisflies (Polycentropodidae), and beetles (Elmidae and Gyrinidae). Our streams lacked large decapod populations, contrasting with other Puerto Rican streams. We found little evidence for an insect effect on leaf breakdown. Results suggest that land use is an important factor affecting leaf litter processing in streams. In contrast to studies in temperate regions, we found little evidence for a positive nutrient related effect of agricultural land use on decomposition rates. Changes in the physical characteristics of streams appear to be the main drivers behind observed decomposition patterns.

An accurate aging clock developed from large-scale gut microbiome and human gene expression data.

Accurate measurement of the biological markers of the aging process could provide an "aging clock" measuring predicted longevity and enable the quantification of the effects of specific lifestyle choices on healthy aging. Using machine learning techniques, we demonstrate that chronological age can be predicted accurately from (1) the expression level of human genes in capillary blood and (2) the expression level of microbial genes in stool samples. The latter uses a very large metatranscriptomic dataset, stool samples from 90,303 individuals, which arguably results in a higher quality microbiome-aging model than prior work. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to have higher model-predicted ages and people on a vegetarian diet tend to have lower model-predicted ages. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model.

Detecting salivary host and microbiome RNA signature for aiding diagnosis of oral and throat cancer.

OBJECTIVE: Oral squamous cell carcinoma (OSCC) and oropharyngeal squamous cell carcinoma (OPSCC) can go undetected resulting in late detection and poor outcomes. We describe the development and validation of CancerDetect for Oral & Throat cancer™ (CDOT), to detect markers of OSCC and/or OPSCC within a high-risk population. MATERIAL AND METHODS: We collected saliva samples from 1,175 individuals who were 50 years or older, or adults with a tobacco use history. 945 of those were used to train a classifier using machine learning methods, resulting in a salivary microbial and human metatranscriptomic signature. The classifier was then independently validated on the 230 remaining samples prospectively collected and unseen by the classifier, consisting of 20 OSCC (all stages), 76 OPSCC (all stages), and 134 negatives (including 14 pre-malignant). RESULTS: On the validation cohort, the specificity of the CDOT test was 94 %, sensitivity was 90 % for participants with OSCC, and 84.2 % for participants with OPSCC. Similar classification results were observed among people in early stage (stages I & II) vs late stage (stages III & IV). CONCLUSIONS: CDOT is a non-invasive test that can be easily administered in dentist offices, primary care centres and specialised cancer clinics for early detection of OPSCC and OSCC. This test, having received FDA's breakthrough designation for accelerated review, has the potential to enable early diagnosis, saving lives and significantly reducing healthcare expenditure.

Pathogen detection and characterization from throat swabs using unbiased metatranscriptomic analyses.

OBJECTIVES: Infectious diseases are common but are not easily or readily diagnosed with current methodologies. This problem is further exacerbated by the constant presence of mutated, emerging, and novel pathogens. One of the most common sites of infection by many pathogens is the human throat. However, there is no universal diagnostic test that can distinguish these pathogens. Metatranscriptomic (MT) analysis of the throat represents an important and novel development in infectious disease detection and characterization, because it is able to identify all pathogens using a fully unbiased approach. METHODS: To test the utility of an MT approach to pathogen detection, throat samples were collected from participants before, during, and after an acute sickness. RESULTS: Clear sickness-associated shifts in pathogenic microorganisms were detected in the patients. Important insights into microbial functions and antimicrobial resistance genes were obtained. CONCLUSION: MT analysis of the throat represents an effective method for the unbiased identification and characterization of pathogens. Because MT data include all microorganisms in the sample, this approach should not only allow the identification of pathogens, but provide an understanding of the effects of the resident throat microbiome in the context of human health and disease.

Gut Metabolites Are More Predictive of Disease and Cohoused States than Gut Bacterial Features in a Polycystic Ovary Syndrome-Like Mouse Model.

Polycystic ovary syndrome (PCOS) impacts ∼10% of reproductive-aged women worldwide. In addition to infertility, women with PCOS suffer from metabolic dysregulation which increases their risk of developing type 2 diabetes, cardiovascular disease, and nonalcoholic fatty liver disease. Studies have shown differences in the gut microbiome of women with PCOS compared to controls, a pattern replicated in PCOS-like mouse models. Recently, using a letrozole (LET)-induced mouse model of PCOS, we demonstrated that cohousing was protective against development of metabolic and reproductive phenotypes and showed via 16S amplicon sequencing that this protection correlated with time-dependent shifts in gut bacteria. Here, we applied untargeted metabolomics and shotgun metagenomics approaches to further analyze the longitudinal samples from the cohousing experiment. Analysis of beta diversity found that untargeted metabolites had the strongest correlation to both disease and cohoused states and that shifts in metabolite diversity were detected prior to shifts in bacterial diversity. In addition, log2 fold analyses found numerous metabolite features, particularly bile acids (BAs), to be highly differentiated between placebo and LET, as well as LET cohoused with placebo versus LET. Our results indicate that changes in gut metabolites, particularly BAs, are associated with a PCOS-like phenotype as well as with the protective effect of cohousing. Our results also suggest that transfer of metabolites via coprophagy occurs rapidly and may precipitate changes in bacterial diversity. This study joins a growing body of research linking changes in primary and secondary BAs to host metabolism and gut microbes relevant to the pathology of PCOS. IMPORTANCE Using a combination of untargeted metabolomics and metagenomics, we performed a comparative longitudinal analysis of the feces collected in a cohousing study with a PCOS-like mouse model. Our results showed that gut metabolite composition experienced earlier and more pronounced differentiation in both the disease model and cohoused mice compared with the microbial composition. Notably, statistical and machine learning approaches identified shifts in the relative abundance of primary and secondary BAs, which have been implicated as modifiers of gut microbial growth and diversity. Network correlation analysis showed strong associations between particular BAs and bacterial species, particularly members of Lactobacillus, and that these correlations were time and treatment dependent. Our results provide novel insights into host-microbe relationships related to hyperandrogenism in females and indicate that focused research into small-molecule control of gut microbial diversity and host physiology may provide new therapeutic options for the treatment of PCOS.

The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer.

Despite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.

A clinically validated human capillary blood transcriptome test for global systems biology studies.

To prevent and treat chronic diseases, including cancer, a global application of systems biology is needed. We report here a whole blood transcriptome test that needs only 50 µl of capillary (fingerprick) blood. This test is suitable for global applications because the samples are preserved at ambient temperature for up to 4 weeks and the RNA preservative inactivates all pathogens, enabling safe transportation. Both the laboratory and bioinformatic steps are automated and performed in a clinical lab, which minimizes batch effects and creates unbiased datasets. Given its clinical testing performance and accessibility to traditionally underrepresented and diverse populations, this test offers a unique ability to reveal molecular mechanisms of disease and enable longitudinal, population-scale studies.

Seasonal dynamics of DNA and RNA viral bioaerosol communities in a daycare center.

BACKGROUND: Viruses play an important role in ecosystems, including the built environment (BE). While numerous studies have characterized bacterial and fungal microbiomes in the BE, few have focused on the viral microbiome (virome). Longitudinal microbiome studies provide insight into the stability and dynamics of microbial communities; however, few such studies exist for the microbiome of the BE, and most have focused on bacteria. Here, we present a longitudinal, metagenomic-based analysis of the airborne DNA and RNA virome of a children's daycare center. Specifically, we investigate how the airborne virome varies as a function of season and human occupancy, and we identify possible sources of the viruses and their hosts, mainly humans, animals, plants, and insects. RESULTS: Season strongly influenced the airborne viral community composition, and a single sample collected when the daycare center was unoccupied suggested that occupancy also influenced the community. The pattern of influence differed between DNA and RNA viromes. Human-associated viruses were much more diverse and dominant in the winter, while the summertime virome contained a high relative proportion and diversity of plant-associated viruses. CONCLUSIONS: This airborne microbiome in this building exhibited seasonality in its viral community but not its bacterial community. Human occupancy influenced both types of communities. By adding new data about the viral microbiome to complement burgeoning information about the bacterial and fungal microbiomes, this study contributes to a more complete understanding of the airborne microbiome.

Exposure to a Healthy Gut Microbiome Protects Against Reproductive and Metabolic Dysregulation in a PCOS Mouse Model.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting ∼10% to 15% of reproductive-aged women worldwide. Diagnosis requires two of the following: hyperandrogenism, oligo-ovulation or anovulation, and polycystic ovaries. In addition to reproductive dysfunction, many women with PCOS display metabolic abnormalities associated with hyperandrogenism. Recent studies have reported that the gut microbiome is altered in women with PCOS and rodent models of the disorder. However, it is unknown whether the gut microbiome plays a causal role in the development and pathology of PCOS. Given its potential role, we hypothesized that exposure to a healthy gut microbiome would protect against development of PCOS. A cohousing study was performed using a letrozole-induced PCOS mouse model that recapitulates many reproductive and metabolic characteristics of PCOS. Because mice are coprophagic, cohousing results in repeated, noninvasive inoculation of gut microbes in cohoused mice via the fecal-oral route. In contrast to letrozole-treated mice housed together, letrozole mice cohoused with placebo mice showed significant improvement in both reproductive and metabolic PCOS phenotypes. Using 16S rRNA gene sequencing, we also observed that the overall composition of the gut microbiome and the relative abundance of Coprobacillus and Lactobacillus differed in letrozole-treated mice cohoused with placebo mice compared with letrozole mice housed together. These results suggest that dysbiosis of the gut microbiome may play a causal role in PCOS and that modulation of the gut microbiome may be a potential treatment option for PCOS.

Sampling, Extraction, and High-Throughput Sequencing Methods for Environmental Microbial and Viral Communities.

The emergence of high-throughput sequencing technologies has deepened our understanding of complex microbial communities and greatly facilitated the study of as-yet uncultured microbes and viruses. Studies of complex microbial communities require high-quality data to generate valid results. Here, we detail current methods of microbial and viral community sample acquisition, DNA extraction, sample preparation, and sequencing on Illumina high-throughput platforms. While using appropriate analytical tools is important, it must not overshadow the need for establishing a proper experimental design and obtaining sufficient numbers of samples for statistical purposes. Researchers must also take care to sample biologically relevant sites and control for potential confounding factors (e.g., contamination).