Sundaresan, M. | bioRxiv Science

bioRxiv2021

Comparative genomics of Bordetella pertussis isolates from New Zealand, a country with an uncommonly high incidence of whooping cough

2.Whooping cough, the respiratory disease caused by Bordetella pertussis, has undergone a wide-spread resurgence over the last several decades. Previously, we developed a pipeline to assemble the repetitive B. pertussis genome into closed sequences using hybrid nanopore and Illumina sequencing. Here, this sequencing pipeline was used to conduct a more high-throughput, longitudinal screen of 66 strains isolated between 1982 and 2018 in New Zealand. New Zealand has a higher incidence of whooping cough than many other countries; usually at least twice as many cases per 100,000 people as the USA and UK and often even higher, despite similar rates of vaccine uptake. To the best of our knowledge, these strains are the first New Zealand B. pertussis isolates to be sequenced. The analyses here show that, on the whole, genomic trends in New Zealand B. pertussis isolates, such as changing allelic profile in vaccine-related genes and increasing pertactin deficiency, have paralleled those seen elsewhere in the world. At the same time, phylogenetic comparisons of the New Zealand isolates with global isolates suggest that a number of strains are circulating in New Zealand which cluster separately from other global strains, but which are closely related to each other. The results of this study add to a growing body of knowledge regarding recent changes to the B. pertussis genome, and are the first genetic investigation into B. pertussis isolates from New Zealand. 3. Impact statementSince the 1990s, whooping cough has been resurgent in many countries around the world, despite the wide availability of vaccines. New Zealand has often had a higher incidence of whooping cough than other countries such as the USA, UK and Australia, both during outbreak periods and in the intervening years. One potential reason for the resurgence of whooping cough is genetic changes to the causative bacterium, Bordetella pertussis, with several recently identified, ongoing global genetic trends. No B. pertussis isolates from New Zealand have previously been sequenced, however. Here, we used hybrid sequencing to investigate the genomes of 66 New Zealand B. pertussis isolates, collected between 1982 and 2018. This revealed that genetic trends in New Zealand B. pertussis match those observed elsewhere, but over the years a number of highly similar or identical strains appear to have circulated (or are currently circulating) in the country, a phenomenon not commonly noted elsewhere. This first study of B. pertussis isolates from New Zealand contributes to the global understanding of B. pertussis genomics, as well as providing the groundwork for any future whole genome sequencing of New Zealand B. pertussis isolates. 4. Data summaryO_LINanopore and Illumina fastq sequence files for all strains have been deposited in NCBIs Sequence Read Archive, BioProject PRJNA556977. A full list of accession numbers for all sequence read files is provided in Supplementary Table S1. C_LIO_LIGenome sequences for 63 strains have been deposited in NCBIs GenBank, BioProject PRJNA556977, accession numbers in Supplementary Table S1. C_LIO_LIGenome assemblies for 3 strains assembled using only nanopore data (NZ1, NZ5 and NZ29), which had a high number of pseudogenes, were not deposited in GenBank, but are available from Figshare: https://doi.org/10.6084/m9.figshare.12640463 C_LIO_LISource code and full commands used are available from Github: https://github.com/nataliering/Comparative-genomics-of-Bordetella-pertussis-isolates-from-New-Zealand C_LI The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

genomics↗

bioRxiv2019

Decoding task-specific cognitive states with slow, directed functional networks in the human brain

Flexible functional interactions among brain regions mediate critical cognitive functions. Such interactions can be measured from functional magnetic resonance imaging (fMRI) data with either instantaneous (zero-lag) or lag-based (time-lagged) functional connectivity; only the latter approach permits inferring directed functional interactions. Yet, the fMRI hemodynamic response is slow, and sampled at a timescale (seconds) several orders of magnitude slower than the underlying neural dynamics (milliseconds). It is, therefore, widely held that lag-based fMRI functional connectivity, measured with approaches like as Granger-Geweke causality (GC), provides spurious and unreliable estimates of underlying neural interactions. Experimental verification of this claim has proven challenging because neural ground truth connectivity is often unavailable concurrently with fMRI recordings. We address this challenge by combining machine learning with GC functional connectivity estimation. We estimated instantaneous and lag-based GC functional connectivity networks using fMRI data from 1000 participants, drawn from the Human Connectome Project database. A linear classifier, trained on either instantaneous or lag-based GC, reliably discriminated among seven different task and resting brain states, with over 80% cross-validation accuracy. With network simulations, we demonstrate that instantaneous and lag-based GC exploited interactions at fast and slow timescales, respectively, to achieve robust classification. With human fMRI data, instantaneous and lag-based GC identified distinct, cognitive core networks. Finally, variations in GC connectivity explained inter-individual variations in a variety of cognitive scores. Our findings show that instantaneous and lag-based methods reveal complementary aspects of functional connectivity in the brain, and suggest that slow, directed functional interactions, estimated with fMRI, provide robust markers of behaviorally relevant cognitive states.\n\nAuthor SummaryFunctional MRI (fMRI) is a leading, non-invasive technique for mapping networks in the human brain. Yet, fMRI signals are noisy and sluggish, and fMRI scans are acquired at a timescale of seconds, considerably slower than the timescale of neural spiking (milliseconds). Can fMRI, then, be used to infer dynamic processes in the brain such as the direction of information flow among brain networks? We sought to answer this question by applying machine learning to fMRI scans acquired from 1000 participants in the Human Connectome Project (HCP) database. We show that directed brain networks, estimated with a technique known as Granger-Geweke Causality (GC), accurately predicts individual subjects task-specific cognitive states inside the scanner, and also explains variations in a variety of behavioral scores across individuals. We propose that directed functional connectivity, as estimated with fMRI-GC, is relevant for understanding human cognitive function.

neuroscience↗