Scientific conferences are most beneficial to participants when they showcase significant new experimental developments, accurately summarize the current state of the field, and provide strong opportunities for collaborative networking. A top-notch slate of invited speakers, assembled by conference organizers or committees, is key to achieving these goals. The underrepresentation of female speakers at prominent scientific meetings was documented with extensive supportive data in 2017. The purpose of this report is to update those data with recent  trends consequent to that transparency, and to suggest the means to further shift speaker selections towards parity. 

Learning Objectives: 

1. Become aware that speaker lists covering 35 years of 4 major virology conferences show historical bias against female speaker selections.
2. Become aware that those same data sets show now how enhanced transparency in selection procedures, can ameliorate  the unconscious bias in meeting organizers that has contributed to those historical trends. 

Vaginal microbial communities play an important role in women’s heath.  It is now clear that the vaginal microbiome is linked to pregnancy complications such as preterm birth and risk for acquisition of sexually transmitted infections including HIV.  The relative simplicity of the vaginal microbiome also makes it an ideal system for piloting new for multi-omic assays and new integrative analytical methods. Multi-omic approaches include 16S rRNA surveys, metagenomics, metatranscriptomics, metaproteomics and metabolomics. New approaches and technologies are rapidly advancing the frontiers of our understanding of host-microbiome interactions. Further efforts to limit sources of bias and increase harmonization across studies are critical for the validation of findings to inform evidence-based medicine.

Learning Objectives

1. Identify commonly used multi-omic approaches.

2: Describe the current challenges and opportunities in multi-omic science.

The emergence and spread of antimicrobial-resistant (AMR) bacteria and resistance mechanisms pose a serious challenge to laboratories in both hospital clinical microbiology and public health settings. Accordingly, elevated vigilance by antibiotic stewardship programs at a local level is required to limit the spread and dissemination of drug-resistant pathogens among clinical patient populations. Whole genome sequencing, as opposed to traditional culturing and isolate typing, is proving to be an effective means to rapidly assess patient samples for the presence of potentially drug-resistant pathogens. The cost of whole genome sequencing continues to decline; however, there is an increasing need for effective bioinformatic analysis pipelines suitable for routine use by clinical microbiology laboratories.

In this presentation, we use QIAGEN’s CLC Microbial Genomics Module, an extension to the CLC Genomics Workbench software platform, and the new QIAGEN Microbial Insights AR Database (QMI-AR), to analyze public gut microbiome data obtained from pediatric stem cell transplantation patients. We demonstrate how CLC Genomics Workbench platform, along with the QMI-AR database, can be used to profile microbiome samples for their constituent bacteria, and identify genomic markers for antimicrobial resistance. Furthermore, we demonstrate how the CLC platform can be used to build customized analysis workflows designed to automate the analysis and reporting of results, an essential component of any laboratory where standard operating procedures and rigorous results reporting are expected and required.

Learning Objectives:

1. Detect antimicrobial resistance genes using multiple methods and databases, including the new QIAGEN Microbial Insights AR database.
2. Determine the taxonomic profile of shotgun metagenomic samples
3. Build reproducible workflows for routine analysis
4. Use tools from CLC Microbial Genomics Module to identify potential mobile AMR markers found in plasmids
5. Visualize these markers in a reference genome using the new Genome Browser

Plants recruit soil microbes that provide nutrients, promote growth and protect against pathogens. However, the full potential of microbial communities for supporting plant health and agriculture is unrealized, in part because rhizosphere members key for plant health are difficult to prioritize. Microbes that ubiquitously associate with a plant species across large spatial scales and varied soil conditions provide a practical starting point for discovering beneficial members. Here, we quantified the structures of bacterial/archaeal and fungal communities in the common bean rhizosphere (Phaseolus vulgaris), and assessed its core membership across two evolutionarily-distinct genotypes, grown in field conditions across five major growing regions in the United States. We discovered a conserved core microbiome of 271 bacterial, archaeal and fungal taxa that were consistently associated with the common bean, despite different soil types, management, climates and genotypes. Neutral models of abundance-occupancy relationships and co-occurrence networks suggest that these core taxa are in intimate relationships with the plant, rather than important members of the local soil microbiome. We expanded our study to leverage rhizosphere samples inclusive of eight additional common bean genotypes that were grown in Colombian soils. Surprisingly, there were 48 persistent bacterial taxa that were detected in all samples, inclusive of U.S. and Colombian-grown beans. Many of the core taxa were yet-uncultured and affiliated with Proteobacteria; these taxa are prime targets for functional investigation in support of sustainable common bean agriculture. More generally, our approach provides insights into microbial taxa that can be prioritized towards translational studies of plant-microbiome. management.

Learning objectives:

1. Rhizosphere is one of the most diverse ecosystems harboring a great number of plant beneficial microbes and functions.
2. Plants consistently recruit a number of ecological important microbial taxa which represent the core microbiome.
Members of the core microbiome have potential to be implemented in plant breeding strategies.

Meningitis is an infection of the meninges, the membranes that surround the brain and spinal cord. Encephalitis is inflammation of the brain itself. Anyone can get meningitis or encephalitis and causes include viruses, bacteria, fungus, and parasites. There are a number of diagnostic challenges for identification of patients with meningitis/encephalitis and there have been changes in the epidemiology in recent years. This presentation will review the different symptoms and etiological agents that cause meningitis and encephalitis as well as provide an overview of the current tools available for diagnosis.

Learning Objectives:

1. Describe the difference between meningitis and encephalitis and the challenges with diagnosis.

2. Describe the most common causes of meningitis and encephalitis.

3. Describe the diagnostic tools used to diagnose meningitis and encephalitis.

Recent molecular advances have unmasked key functions of our microbial kin, our microbiota, and its significant role in our disease and heath, our wellness.  None has matched the interest of the GUT microbiota and its co- functionality with the brain, defining an interplay maximized in  costly age related diseases. The Europeans redefined aging as a disease in 2015, the US via the NIH shortly thereafter in 2016.  Not without controversy, but all highlighting the incredible importance of a balanced, healthy microbiota in aging via the Hologenomic Theory (www.globalbugs.com/Aging and Hologenomic Center) , as Dual Citizens,  birth to death, the loss of microbes,  including fungi,  key in redefining this pathologic process. As of 2035, greater than half of the worlds’ population will be over 65.

Here, we will highlight this interplay using  2 clocks, the first our 4 quadrant  Microbial Clock (Microbes), followed by the Biologic clock (Humans), both contributing to a common pathway for  metabolic diseases,  immune-scenesence a common pathway. Six brain associated diseases will be a focus via the Gut Brain Axis, catalyzing key mechanisms  of aging, longevity, and “turning back the clock”.  Is it possible?  Dexter, the Dual Faced  Germ-man Puppet (www.globalbugs.com/Teaching with Puppets) will illustrate the importance of both macro (Humans) and micro (microbes)  Holobionts, now defined by available genetic tools and machine learning, creating a Personal Signature.

Finally, we will discuss intervention of brain functionality via banking terms recognizing the similarities of investment banking and the use of pre/probiotics and diet as mechanisms of leveraging and rejuvenating your microbiome and wellness. Microbial Based Economics (MBE). The GUT is  our banking investment manager and key in creating a balanced microbial portfolio, particularly in age related diseases.
(www.globalbugs.com/Probiotic Solutions) How should you invest, when and for how long and is precision medicine the key to maximizing microbial wellness.  Ultimately is “Aging longer, well, thru microbiology ‘ possible?  

Learning Objectives.

1. Unmask the growing awareness of our microbiota in  wellness and disease, implemented via  a common pathway, most noticeably on brain  functionality, highlighting aging as a reclassified  disease.

2. Promote the concept of Microbial Banking with Microbial Based Economics, catalyzing the importance  of the GUT microbiota as our investment banker in aging , promoting a lifelong beneficial microbial portfolio, potentially  reconstructed and rejuvenated by precision medicine via synbiotics and or diet.   

Background:  The vast majority of all genes are contained within the genomes of the prokaryotes, including the eubacteria and the archaea.  These largely single-cellular domains of life thus contain most of the metabolic machinery housed within the earth’s biosphere.  The gene systems that encode this machinery include entire pathways for the biosynthesis and catabolism of literally millions of natural products.  A small subset of species within the eubacteria are also associated with disease as pathogens, and these organisms produce a specialized set of secondary metabolic products termed virulence factors.  The vast majority of all prokaryotic genes are either unannotated or under-annotated with respect to the functions of their encoded proteins.  

Rationale:  To develop computational means to identify the specific genes, and the metabolic pathways that they encode, that underlie traits of interest for the manipulation of prokaryotic physiology to improve human life and health.

Specific Aim of the current research: To develop generic unbiased computational means to identify unannotated bacterial genes associated with pathogenesis, virulence and tissue tropism.

Results:  Our initial tools for the identification of novel bacterial virulence genes were adopted from the statistical genetic approaches used in eukaryotic gene mapping.  Following the statistical identification of our first set of candidate unannotated virulence genes from the human obligate pathogen Haemophilus influenzae, we demonstrated using a combination of in vitro and in vivo animal model experiments that the identified genes’ cognate proteins were actual virulence factors.  Follow-on studies of one of these novel proteins, Msf1, provided mechanistic details regarding its mode of action.   Subsequently, we developed random forest and neural network-based machine-learning approaches for a more thorough search of H. influenzae’s virulence/tropism genes.  Through multiple rounds of parameter tuning we developed a highly reliable random forest program that provided greater than 85% specificity with regard to determining the actual disease (out of five diseases) from which a given bacterial strain was isolated.  Examination of the random forest’s classifier gene selection provides a rich source of novel unannotated genes from within the microbial genomic dark matter that will provide for a focused approach for the characterization of much new biology relating to pathogenesis.  It is interesting to note that four of the top five genes used by the classifier have no annotation whatsoever.  Using a second neural net approach, in this case for protein annotation we have been able to assign, with a high degree of confidence, at least one GO (gene ontology) term for 14% of the 13,692 hypothetical proteins encoded by the Moraxella catarrhalis pan (supra) genome.

Discussion:  Through the combination of multiple machine learning algorithms we have developed the beginnings of a pipeline for the in silico identification and characterization of novel unannotated genes.

Conclusions:  The development of high-throughput whole genome sequencing together with the creation of a suite of unbiased methods for the identification and characterization of unannotated prokaryotic genes that are associated with specific measurable traits will provide a universal method for targeted gene characterization leading to the discovery of novel biology underlying any metabolic process of interest.

Learning Objectives:

1. Understand that even in this day of massively high throughput whole genome sequencing that the vast majority of prokaryotic genes and gene systems are completely unannotated meaning that we have no idea what the genes that we sequence encode.
2. Through the application of machine-learning (artificial intelligence) approaches we are beginning the development of a computational pipeline that can be used to: (a) identify the genes involved in a particular process; and b) then annotate the identified genes as to likely molecular function.

immunocompromised populations where the virus can cause systemic and even fatal infections. Indeed, we have shown that astrovirus infections are more prevalent than norovirus in our pediatric oncology population with many patients being long-term shedders. Due to the lack of cell culture systems and animal models for most astrovirus family members, little is known about pathogenesis. Our group demonstrated that the human astroviruses cause intestinal epithelial cells to lose cell-to-cell junctions resulting in increased membrane permeability (i.e. leaky gut). The virus does not have to replicate to induce membrane permeability. The outer viral coat protein, or capsid, is all that was required. To determine if this could also happen in vivo, animal models were orally inoculated with species-specific astrovirus or astrovirus capsid and intestinal permeability and clinical disease was monitored. Like cells in culture, the astrovirus capsid protein alone caused a loss of membrane permeability, relocalization of sodium transporters and diarrhea. Our initial work suggests the virus disrupts the barrier by causing the epithelial cells to change and undergo epithelial to mesenchymal transition.

Learning Objectives:

1. Recognize the importance of astroviruses.

2. Illustrate how a viral toxin could cause diarrhea without causing damage to the intestinal cells.

In contrast to other countries in Latin America, Peru had been notoriously spared by the global dissemination of carbapenem-resistant Klebsiella pneumoniae (CR-Kp) until recently. Isolated cases of KPC-producing K. pneumoniae have been reported since 2013 in Lima. However, by 2017, rapid emergence of CR-Kp occurred  at a tertiary care hospital in Lima. Here, we  describe the molecular characteristics of the CR-Kp isolates recovered during this emergence. We conducted a retrospective study of the emerging cases of CR-Kp infections at a tertiary hospital in Lima, from July 2015 to December 2017. Kp was isolated from various samples and profiled using the VITEK system and standard biochemical tests. Whole genome sequencing was performed using an Illumina MiSeq instrument at 50–100x coverage and bioinformatic analysis was conducted using CLC Genomics Workbench v12 (QIAGEN Bioinformatics). De novo assemblies were generated to identify virulence and antibiotic resistance genes. Reference-based mappings were used to infer maximum-likelihood phylogenies from SNP alignments. Fifty-nine unique CR-Kp cases were identified during the study period. A significant increase in the number of cases was observed in 2017 compared to previous years. Carbapenemase genes of were detected in 40 isolates (95%);  24 (60%) were blaNDM-1, 13 (32%) were blaKPC-2 and 3 (8%) were blaIMP-16. Isolates carrying blaKPC were more prevalent during years 2015–2016 and blaNDM isolates were more prevalent during 2017 (p<0.0001). Multiple sequence types were identified carrying the KPC, NDM and various extended-spectrum beta-lactamases, suggesting that the increase in cases was due to infection from multiple carbapenem-resistant clones. We show that multiclonal expansion of CR-Kp strains has occurred at our institution and blaKPC has been replaced by blaNDM as the prevalent resistance mechanism since 2017.

Fiorella Krapp1,2, Coralith García1,2, Karen Ocampo2, Lizeth Astocondor2, Catherine Amaro3, Pablo
Tsukayama2,4
1 Facultad de Medicina, Universidad Peruana Cayetano Heredia, Lima, Peru
2 Instituto de Medicina Tropical “Alexander von Humbold”, Universidad Peruana Cayetano Heredia, Lima, Peru
3 Hospital Nacional Cayetano Heredia, Lima, Peru
4 Facultad de Ciencias, Universidad Peruana Cayetano Heredia, Lima, Peru

Canine parvovirus (CPV) is an important pathogen causing severe diseases in dogs, including acute hemorrhagic enteritis, myocarditis and cerebellar disease. Cross-species transmission of CPV occurs as a result of mutations on the viral capsid surface that alter the species-specific binding to the host receptor, transferrin receptor type-1 (TfR). The interaction between CPV and TfR has been extensively studied and previous analyses have suggested the CPV-TfR complex is asymmetric. To enhance the understanding of the underlying molecular mechanisms, we determined the CPV-TfR interaction using cryo electron microscopy to solve the icosahedral (3.0 Å resolution) and asymmetric (5.0 Å resolution) complex structures. Structural analyses revealed conformational variations of the TfR molecules relative to the binding site, which translated into dynamic molecular interactions between CPV and TfR. For the first time, the precise footprint of the receptor on the virus capsid was identified along with the identity of the amino acid residues in the virus-receptor interface. Our ‘rock and roll’ model provides a novel explanation for previous findings and gives new insights into species jumping and the variation in host ranges associated with new pandemics in dogs.

Learning Objectives:

1) Virus binding to receptor is more dynamic than previously thought.
2) Receptor binding causes changes to the overall virus capsid.

Traditional pathogen detection methods rely on the identification of pathogens known to be associated with a particular syndrome. In many cases, it involves complex methodology, lengthy procedure and time-consuming operation. Also, traditional methods often fail to identify mixed pathogens in complex clinical samples, making the diagnosis and treatment of infection challenging. As the cost of sequencing drops and technology improves, metagenomics becomes feasible. In this webinar, Dr. Kin Kuok will introduce MGI’s DNA nanoball sequencing (DNBSEQTM) and MGI’s microbial detection total solution. followed by several case studies explaining how MGI’s total solution enables fast, accurate and comprehensive microbial detection.

Learning objectives:

1. MGI is a subsidiary of BGI group. MGI’s DNA nanoball sequencing (DNBSEQTM) provides researchers an alternative sequencing platform to avoid sample contamination/index hopping and achieve higher sequencing accuracy.

2. MGI’s microbial detection total solution supports massive screening on unknown pathogens in human or animal infection, allowing accurate diagnosis and treatment.

Fungi and bacteria form different types of associations that are central to numerous environmental processes. Similar to human dysbioses and microbiome manipulation via antibiotics and probiotic remediation, it may be possible to steer agricultural ecosystem function by altering soil environmental parameters to select for desired microbes and traits. As a first step, we wish to investigate and characterize bacterial interactions with fungi at a fundamental level, to establish the types of interactions, their breadth both phylogenetically and functionally, and later, to characterize these interactions at the molecular level. Prior work suggests that bacteria and fungi exploit each other both on the surfaces of fungal mycelial networks, as well as within mycelia. We begin by exploring the diverse nature of these associations by screening both genomic databases as well as culture collections. Available fungal genome projects are screened for the presence of bacterial genomic signatures that may have been inadvertently sequenced. Challenges in this field of taxonomy classification and bacterial identification will be discussed. In addition, we screen diverse fungal collections for bacterial signatures using an approach based on sequencing of the 16S rRNA gene combined with microscopic confirmation of the presence of endohyphal bacteria. For some isolates, we explore some of the interactions phenotypically, by monitoring growth during confrontation assays. We have used devices dubbed ‘fungal highway’ columns, to isolate bacteria capable of utilizing fungal mycelia as a dispersal mechanism, and are testing a 3D fabrication of this type of device for routine exploration of such interactions. Growth phenotypes of some of these bacterial and fungal isolates, as well as known interacting bacterial-fungal pairs, are being examined. Collectively, these studies begin to shed light into the diversity, and range of interactions that occur among these dominant microbial players.  

https://github.com/LANL-Bioinformatics
https://genomicscience.energy.gov/research/sfas/lanlbfi.shtml
This study was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Division, under award numbers LANLF59T and LANLF59C.

Learning objectives:
1. Metagenomic analysis is already complex, and complicated further by incomplete and non-standardized databases of known organisms
2. fungi may have their own 'microbiomes', as we discover signatures of bacteria both within and on the surface of fungal hyphae

Jonathan is a co-founder and the CEO of FitBiomics, a biotechnology startup that is sequencing the microbiome of elite athletes to identify and isolate next-generation probiotics for applications in consumer health and nutrition. He will talk about research recently published in Nature Medicine, regarding discovery and functional validation of a performance enhancing microbe that eats lactic acid and promotes endurance. This work was done while Jonathan was a postdoctoral fellow George Church's lab at the Wyss Institute for Biologically Inspired Engineering, at Harvard, where he worked on transformative sequencing technologies and programmable cellular engineering.  Having played Division I college basketball at St. John’s University and coached inner city public high school basketball, one of Jonathan’s long-term goals is to use sports as a medium to inspire the next generation of scientists.

Learning Objectives:

1. Learn how next-generation sequencing can identify novel probiotic species and strains
2. Learn how athletes’ have distinct microbial communities that are dynamic during performance and recovery phases

Antibiotics have radically transformed our world by making previously incurable illnesses treatable. Millions of lives have been saved and our wellbeing significantly improved as a result. This remarkable progress is threatened by the spread of antibiotic resistance and requires an urgent global response. In this webinar, Dr Manica Balasegaram, GARDP’s Executive Director shares GARDP’s goal to accelerate the development and delivery of five new and improved treatments by 2025 to address antibiotic-resistant infections that pose the greatest threat to health and development.

MVRSION is a bench and informatic system which employs simultaneous weighted analysis eight of nine 16S hyper variable regions. We use Fluidigm Juno to amplify and index these regions in up to 192 samples per run. These are sequenced and analyzed in parallel, using the MVRSION computational protocol. Both accuracy and economy exceed other 16S methods, such as QIIME.

Learning Objectives: 

1. Review of 16S rRNA gene sequencing which allows for microbiome characterization with less sequencing/cost than whole genome sequencing
2. Method for weighted analysis of almost all the hype rvariable regions