Leveraging crowdsourcing to accelerate global health solutions

Evaluation of the antibacterial activity of Elsholtzia ciliate essential oil against halitosis-related Fusobacterium nucleatum and Porphyromonas gingivalis

The broad-spectrum antimicrobial activity of Elsholtzia ciliate essential oil (ECO) has been previously reported, but its effectiveness against halitosis-causing bacteria such as Fusobacterium nucleatum and Porphyromonas gingivalis is not well understood. In this study, we investigated the bacteriostatic activity of ECO against planktonic cells and biofilms of F. nucleatum and P. gingivalis, as well as its ability to inhibit bacterial metabolism and production of volatile sulfur compounds (VSCs) at sub-lethal concentrations. Our findings revealed that ECO exhibited comparable activities to chlorhexidine against these oral bacteria. Treatment with ECO significantly reduced the production of VSCs, including hydrogen sulfide, dimethyl disulfide, and methanethiol, which are major contributors to bad breath. As the major chemical components of ECO, carvacrol, p-cymene, and phellandrene, were demonstrated in vitro inhibitory effects on F. nucleatum and P. gingivalis, and their combined use showed synergistic and additive effects, suggesting that the overall activity of ECO is derived from the cumulative or synergistic effect of multiple active components. ECO was found to have a destructive effect on the bacterial cell membrane by examining the cell morphology and permeability. Furthermore, the application of ECO induced significant changes in the bacterial composition of saliva-derived biofilm, resulting in the elimination of bacterial species that contribute to halitosis, including Fusobacterium, Porphyromonas, and Prevotella. These results provide experimental evidence for the potential clinical applications of ECOs in the prevention and treatment of halitosis.

Lessons Learned from Natural Disasters around Digital Health Technologies and Delivering Quality Healthcare

As climate change drives increased intensity, duration and severity of weather-related events that can lead to natural disasters and mass casualties, innovative approaches are needed to develop climate-resilient healthcare systems that can deliver safe, quality healthcare under non-optimal conditions, especially in remote or underserved areas. Digital health technologies are touted as a potential contributor to healthcare climate change adaptation and mitigation, through improved access to healthcare, reduced inefficiencies, reduced costs, and increased portability of patient information. Under normal operating conditions, these systems are employed to deliver personalised healthcare and better patient and consumer involvement in their health and well-being. During the COVID-19 pandemic, digital health technologies were rapidly implemented on a mass scale in many settings to deliver healthcare in compliance with public health interventions, including lockdowns. However, the resilience and effectiveness of digital health technologies in the face of the increasing frequency and severity of natural disasters remain to be determined. In this review, using the mixed-methods review methodology, we seek to map what is known about digital health resilience in the context of natural disasters using case studies to demonstrate what works and what does not and to propose future directions to build climate-resilient digital health interventions.

Potential binding modes of the gut bacterial metabolite, 5-hydroxyindole, to the intestinal L-type calcium channels and its impact on the microbiota in rats

Intestinal microbiota and microbiota-derived metabolites play a key role in regulating the host physiology. Recently, we have identified a gut-bacterial metabolite, namely 5-hydroxyindole, as a potent stimulant of intestinal motility via its modulation of L-type voltage-gated calcium channels located on the intestinal smooth muscle cells. Dysregulation of L-type voltage-gated calcium channels is associated with various gastrointestinal motility disorders, including constipation, making L-type voltage-gated calcium channels an important target for drug development. Nonetheless, the majority of currently available drugs are associated with alteration of the gut microbiota. Using 16S rRNA sequencing this study shows that, when administered orally, 5-hydroxyindole has only marginal effects on the rat cecal microbiota. Molecular dynamics simulations propose potential-binding pockets of 5-hydroxyindole in the α1 subunit of the L-type voltage-gated calcium channels and when its stimulatory effect on the rat colonic contractility was compared to 16 different analogues, ex-vivo, 5-hydroxyindole stood as the most potent enhancer of the intestinal contractility. Overall, the present findings imply a potential role of microbiota-derived metabolites as candidate therapeutics for targeted treatment of slow intestinal motility-related disorders including constipation.

Low microbiome diversity in threatened amphibians from two biodiversity hotspots

Microbial diversity positively influences community resilience of the host microbiome. However, extinction risk factors such as habitat specialization, narrow environmental tolerances, and exposure to anthropogenic disturbance may homogenize host-associated microbial communities critical for stress responses including disease defense. In a dataset containing 43 threatened and 90 non-threatened amphibian species across two biodiversity hotspots (Brazil's Atlantic Forest and Madagascar), we found that threatened host species carried lower skin bacterial diversity, after accounting for key environmental and host factors. The consistency of our findings across continents suggests the broad scale at which low bacteriome diversity may compromise pathogen defenses in species already burdened with the threat of extinction.

Adipocere formation in biofilms as a first step in soft tissue preservation

The preservation of soft tissue in the fossil record is mostly due to the replacement of organic structures by minerals (e.g. calcite, aragonite or apatite) called pseudomorphs. In rare cases soft tissues were preserved by pyrite. We assume that adipocere, as the shaping component, might be a preliminary stage in the pyritisation of soft tissues under anaerobic conditions. Using high-performance liquid chromatography coupled to ultraviolet and mass spectrometric detection (HPLC–UV/MS) and confocal Raman spectroscopy (CRS) we were able to demonstrate the transformation of the hepatopancreas (digestive gland) of the crayfish Cambarellus diminutus [Hobbs 1945] into adipocere within only 9 days, just inside a biofilm. Microorganisms (bacteria and fungi) which were responsible for the biofilm (Sphaerotilus [Kutzig 1833] and Pluteus [Fries 1857]) and maybe the adipocere formation (Clostridium [Prazmowski 1880]) were detected by 16S rRNA gene amplicon sequencing. Furthermore, micro-computed tomography (µ-CT) analyses revealed a precipitation of calcite and further showed that in animals with biofilm formation calcite precipitates in finer grained crystals than in individuals without biofilm formation, and that the precipitates were denser and replicated the structures of the cuticles better than the coarse precipitates.

Fifteen shades of clay: distinct microbial community profiles obtained from bentonite samples by cultivation and direct nucleic acid extraction

Characterizing the microbiology of swelling bentonite clays can help predict the long-term behaviour of deep geological repositories (DGRs), which are proposed as a solution for the management of used nuclear fuel worldwide. Such swelling clays represent an important component of several proposed engineered barrier system designs and, although cultivation-based assessments of bentonite clay are routinely conducted, direct nucleic acid detection from these materials has been difficult due to technical challenges. In this study, we generated direct comparisons of microbial abundance and diversity captured by cultivation and direct nucleic acid analyses using 15 reference bentonite clay samples. Regardless of clay starting material, the corresponding profiles from cultivation-based approaches were consistently associated with phylogenetically similar sulfate-reducing bacteria, denitrifiers, aerobic heterotrophs, and fermenters, demonstrating that any DGR-associated growth may be consistent, regardless of the specific bentonite clay starting material selected for its construction. Furthermore, dominant nucleic acid sequences in the as-received clay microbial profiles did not correspond with the bacteria that were enriched or isolated in culture. Few core taxa were shared among cultivation and direct nucleic acid analysis profiles, yet those in common were primarily affiliated with Streptomyces, Micrococcaceae, Bacillus, and Desulfosporosinus genera. These putative desiccation-resistant bacteria associated with diverse bentonite clay samples can serve as targets for experiments that evaluate microbial viability and growth within DGR-relevant conditions. Our data will be important for global nuclear waste management organizations, demonstrating that identifying appropriate design conditions with suitable clay swelling properties will prevent growth of the same subset of clay-associated bacteria, regardless of clay origin or processing conditions.

Modeling Plasmodium falciparum Diagnostic Test Sensitivity Using Machine Learning With Histidine-Rich Protein 2 Variants

Malaria, predominantly caused by Plasmodium falciparum, poses one of largest and most durable health threats in the world. Previously, simplistic regression-based models have been created to characterize malaria rapid diagnostic test performance, though these models often only include a couple genetic factors. Specifically, the Baker et al., 2005 model uses two types of particular repeats in histidine-rich protein 2 (PfHRP2) to describe a P. falciparum infection, though the efficacy of this model has waned over recent years due to genetic mutations in the parasite. In this work, we use a dataset of 100 P. falciparum PfHRP2 genetic sequences collected in Ethiopia and derived a larger set of motif repeat matches for use in generating a series of diagnostic machine learning models. Here we show that the usage of additional and different motif repeats in more sophisticated machine learning methods proves effective in characterizing PfHRP2 diversity. Furthermore, we use machine learning model explainability methods to highlight which of the repeat types are most important with regards to rapid diagnostic test sensitivity, thereby showcasing a novel methodology for identifying potential targets for future versions of rapid diagnostic tests.

A synthetic probiotic engineered for colorectal cancer therapy modulates gut microbiota

BACKGROUND

Successful chemoprevention or chemotherapy is achieved through targeted delivery of prophylactic agents during initial phases of carcinogenesis or therapeutic agents to malignant tumors. Bacteria can be used as anticancer agents, but efforts to utilize attenuated pathogenic bacteria suffer from the risk of toxicity or infection. Lactic acid bacteria are safe to eat and often confer health benefits, making them ideal candidates for live vehicles engineered to deliver anticancer drugs.

RESULTS

In this study, we developed an effective bacterial drug delivery system for colorectal cancer (CRC) therapy using the lactic acid bacterium Pediococcus pentosaceus. It is equipped with dual gene cassettes driven by a strong inducible promoter that encode the therapeutic protein P8 fused to a secretion signal peptide and a complementation system. In an inducible CRC cell-derived xenograft mouse model, our synthetic probiotic significantly reduced tumor volume and inhibited tumor growth relative to the control. Mice with colitis-associated CRC induced by azoxymethane and dextran sodium sulfate exhibited polyp regression and recovered taxonomic diversity when the engineered bacterium was orally administered. Further, the synthetic probiotic modulated gut microbiota and alleviated the chemically induced dysbiosis. Correlation analysis demonstrated that specific bacterial taxa potentially associated with eubiosis or dysbiosis, such as Akkermansia or Turicibacter, have positive or negative relationships with other microbial members.

CONCLUSIONS

Taken together, our work illustrates that an effective and stable synthetic probiotic composed of P. pentosaceus and the P8 therapeutic protein can reduce CRC and contribute to rebiosis, and the validity and feasibility of cell-based designer biopharmaceuticals for both treating CRC and ameliorating impaired microbiota. Video abstract.

CSVS, a crowdsourcing database of the Spanish population genetic variability

The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network. CSVS can be accessed at: http://csvs.babelomics.org/.

'If I had a hedge fund, I would cure diabetes': endogenous mechanisms for creating public goods

We consider the problem of organizing capital to produce public goods with broad societal value. We review why market corrections via government subsidies or philanthropic initiatives are inadequate, in addition to considering the paradox of patents. Our proposed mechanism (an Ever-growing Prize and a Patent Repository) directs capital towards two innovation problems routinely overlooked: (1) problems for which the reward is insufficient even with established mechanisms (e.g. patents or academic prestige), and (2) problems for which the reward is large, but the effort risk is incalculable. The proposed hedge fund mechanism facilitates crowdsourcing, addressing the challenge of determining problems with broad societal interest; the ever-growing prize allows for an emergent rather than predetermined reward; the patent repository turns private intellectual property into a public good for target problems while circumventing the inventors' threat of patent expiration. We guide this discussion by considering two problems: treating Cystic Fibrosis and curing Diabetes.

Crowdsourcing and open innovation in drug discovery: recent contributions and future directions

The past decade has seen significant growth in the use of 'crowdsourcing' and open innovation approaches to engage 'citizen scientists' to perform novel scientific research. Here, we quantify and summarize the current state of adoption of open innovation by major pharmaceutical companies. We also highlight recent crowdsourcing and open innovation research contributions to the field of drug discovery, and interesting future directions.

Bio-priming with a hypovirulent phytopathogenic fungus enhances the connection and strength of microbial interaction network in rapeseed

Plant disease is one of the most important causes of crop losses worldwide. The effective control of plant disease is related to food security. Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum leads to serious yield losses in rapeseed (Brassica napus) production. Hypovirulent strain DT-8 of S. sclerotiorum, infected with Sclerotinia sclerotiorum hypovirulence-associated DNA virus 1 (SsHADV-1), has the potential to control SSR. In this study, we found rapeseed bio-priming with strain DT-8 could significantly decrease the disease severity of SSR and increase yield in the field. After bio-priming, strain DT-8 could be detected on the aerial part of the rapeseed plant. By 16S rRNA gene and internal transcribed spacer (ITS) sequencing technique, the microbiome on different parts of the SSR lesion on bioprimed and non-bioprimed rapeseed stem was determined. The results indicated that SSR and bio-priming treatment could influence the structure and composition of fungal and bacterial communities. Bio-priming treatment could reduce the total abundance of possible plant pathogens and enhance the connectivity and robustness of the interaction network at the genus level. This might be one of the mechanisms that rapeseed bioprimed with strain DT-8 had excellent tolerance on SSR. It might be another possible mechanism of biocontrol and will provide a theoretical guide for agricultural practical production.

Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Opportunities for Artificial Intelligence in Advancing Precision Medicine

PURPOSE OF REVIEW

We critically evaluate the future potential of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in precision medicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health.

RECENT FINDINGS

High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays; libraries of medical images; or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single-cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine.

SUMMARY

Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.