No abstract

IMPORTANCE Rituximab is among the most frequently used immunotherapies in pediatrics. Few studies have reported long-term adverse events associated with its use for children. OBJECTIVE To describe the use of rituximab and to assess whether its use is associated with shortor long-term adverse events, infections, or time to immune reconstitution in a diverse group of young people. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included 468 patients aged younger than 21 years who received rituximab for diverse indications between October 1, 2010, and December 31, 2017, at Texas Children's Hospital, a large pediatric referral hospital. Patterns of adverse events, infections, and immune recovery are described. Data analyses were conducted from December 2019 to June 2020. EXPOSURE One or more doses of rituximab. MAIN OUTCOMES AND MEASURES Adverse drug events (eg, anaphylaxis), incidence of mild and severe infections, and time to recovery of B lymphocyte subset counts and immunoglobulin levels. Survival models and logistic regression analyses and were used to identify associated risk factors of infectious and noninfectious adverse drug events. RESULTS We identified 468 patients receiving at least 1 dose of rituximab. The total follow-up time was 11 713 person-months. Of the 468 patients, 293 (62.6%) were female, the median (interquartile range) age at receipt of dose was 14.3 (9.9-16.8) years, and 209 (44.7%) were self-reported White Hispanic. Adverse events associated with rituximab infusion occurred in 72 patients (15.4%), and anaphylaxis occurred in 17 patients (3.6%). Long-term adverse events, such as prolonged neutropenia and leukoencephalopathy, were absent. Infections occurred in 224 patients (47.9%); 84 patients (17.9%) had severe infections, and 3 patients (0.6%) had lethal infections. Concurrent use of intravenous chemotherapy, treatment of systemic lupus erythematosus, neutropenia, and use of intravenous immunoglobulin were associated with increased risk of infection. Among 135 patients (28.8%) followed up to B cell count recovery, CD19 + or CD20 + cell numbers normalized in a median of 9.0 months (interquartile range, 5.9-14.4 months) following rituximab use; 48 of 95 patients (51%) evaluated beyond a year had low-forage B cell counts. Recovery of CD27 + memory B cell number occurred in a median of 15.7 months (interquartile range, 6.0-22.7 months). Among patients with normal baseline values, low immunoglobulin G (IgG) levels developed in 67 of 289 patients (23.2%) and low IgM levels in 118 of 255 patients (40.8%); of these patients evaluated beyond 12 months from rituximab, 16 of 117 (13.7%) had persistently low IgG and 37 (33.9%) of 109 had persistently low IgM.

16S rRNA based analysis is the established standard for elucidating microbial community composition. While short read 16S analyses are largely confined to genus-level resolution at best since only a portion of the gene is sequenced, full-length 16S sequences have the potential to provide species-level accuracy. However, existing taxonomic identification algorithms are not optimized for the increased read length and error rate of long-read data. Here we present Emu, a novel approach that employs an expectation-maximization (EM) algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from one simulated data set and two mock communities prove Emu capable of accurate microbial community profiling while obtaining fewer false positives and false negatives than alternative methods. Additionally, we illustrate a real-world application of our new software by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow to those returned by full-length 16S sequences processed with Emu.

No abstract

Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.

Traumatic brain injury (TBI) causes neuroinflammation and neurodegeneration, both of which increase the risk and accelerate the progression of Alzheimer’s disease (AD). The gut microbiome is an essential modulator of the immune system, impacting the brain. AD has been related with reduced diversity and alterations in the community composition of the gut microbiota. This study aimed to determine whether the gut microbiota from AD mice exacerbates neurological deficits after TBI in control mice. We prepared fecal microbiota transplants from 18 to 24 month old 3×Tg-AD (FMT-AD) and from healthy control (FMT-young) mice. FMTs were administered orally to young control C57BL/6 (wild-type, WT) mice after they underwent controlled cortical impact (CCI) injury, as a model of TBI. Then, we characterized the microbiota composition of the fecal samples by full-length 16S rRNA gene sequencing analysis. We collected the blood, brain, and gut tissues for protein and immunohistochemical analysis. Our results showed that FMT-AD administration stimulates a higher relative abundance of the genus Muribaculum and a decrease in Lactobacillus johnsonii compared to FMT-young in WT mice. Furthermore, WT mice exhibited larger lesion, increased activated microglia/macrophages, and reduced motor recovery after FMT-AD compared to FMT-young one day after TBI. In summary, we observed gut microbiota from AD mice to have a detrimental effect and aggravate the neuroinflammatory response and neurological outcomes after TBI in young WT mice.

Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs, "composition-to-function" mappings could be created that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a generalizable genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pooled libraries of DNA constructs of arbitrary length. We show that CLASSIC can measure expression profiles of >105drug-inducible gene circuit designs (ranging from 6-9 kb) in a single experiment in human cells. Using statistical inference and machine learning (ML) approaches, we demonstrate that data obtained with CLASSIC enables predictive modeling of an entire circuit design landscape, offering critical insight into underlying design principles. Our work shows that by expanding the throughput and understanding gained with each design-build-test-learn (DBTL) cycle, CLASSIC dramatically augments the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.

Concussions, both single and repetitive, during contact sports cause brain and body alterations in athletes. The role of the brain-gut connection and changes in the microbiota have not been well established after a head injury or concussion-related health consequences. We recruited 33 Division I Collegiate football players and collected blood, stool, and saliva samples throughout the athletic season. Analysis of the gut microbiome reveals a decrease in abundance for two bacterial species, Eubacterium rectale and Anaerostipes hadrus, after a diagnosed concussion. No significant differences were found regarding the salivary microbiome. Serum biomarker analysis shows an increase in GFAP blood levels in athletes during athletic activity. Additionally, S100β and SAA blood levels were positively correlated with the abundance of Eubacterium rectale species among athletes exposed to subconcussive impacts. These novel findings provide evidence that detecting changes in the gut microbiome may pave the way for improved concussion diagnosis following head injury.