Michael Tartakovsky

PET/CT imaging correlates with treatment outcome in patients with multidrug-resistant tuberculosis

PET/CT imaging in humans with TB correlates with drug response and final treatment outcomes. Visualizing Drug Responses in TB A pair of papers by Chen et al. and Coleman et al. investigate how changes in quantitative positron emission tomography/computed tomography (PET/CT) scans in both nonhuman primates and humans can be used as early surrogate markers of treatment efficacy in tuberculosis. The Coleman et al. study shows that treatment of Mtb-infected macaques with linezolid and the second-generation oxazolidinone AZD5847 resulted in a reduced bacterial load at necropsy and reduced FDG PET avidity and CT-quantified lung pathology. Similar PET/CT changes were seen in human patients infected with extensively drug-resistant Mtb and treated with linezolid. The companion study by Chen et al. corroborated this effect in a prospective analysis of patients with multidrug-resistant tuberculosis and demonstrated that early PET/CT changes predicted final treatment outcomes. Definitive clinical trials of new chemotherapies for treating tuberculosis (TB) require following subjects until at least 6 months after treatment discontinuation to assess for durable cure, making these trials expensive and lengthy. Surrogate endpoints relating to treatment failure and relapse are currently limited to sputum microbiology, which has limited sensitivity and specificity. We prospectively assessed radiographic changes using 2-deoxy-2-[18F]-fluoro-d-glucose (FDG) positron emission tomography/computed tomography (PET/CT) at 2 and 6 months (CT only) in a cohort of subjects with multidrug-resistant TB, who were treated with second-line TB therapy for 2 years and then followed for an additional 6 months. CT scans were read semiquantitatively by radiologists and were computationally evaluated using custom software to provide volumetric assessment of TB-associated abnormalities. CT scans at 6 months (but not 2 months) assessed by radiologist readers were predictive of outcomes, and changes in computed abnormal volumes were predictive of drug response at both time points. Quantitative changes in FDG uptake 2 months after starting treatment were associated with long-term outcomes. In this cohort, some radiologic markers were more sensitive than conventional sputum microbiology in distinguishing successful from unsuccessful treatment. These results support the potential of imaging scans as possible surrogate endpoints in clinical trials of new TB drug regimens. Larger cohorts confirming these results are needed.

Differential Virulence and Disease Progression following Mycobacterium tuberculosis Complex Infection of the Common Marmoset (Callithrix jacchus)

ABSTRACT Existing small-animal models of tuberculosis (TB) rarely develop cavitary disease, limiting their value for assessing the biology and dynamics of this highly important feature of human disease. To develop a smaller primate model with pathology similar to that seen in humans, we experimentally infected the common marmoset (Callithrix jacchus) with diverse strains of Mycobacterium tuberculosis of various pathogenic potentials. These included recent isolates of the modern Beijing lineage, the Euro-American X lineage, and M. africanum. All three strains produced fulminant disease in this animal with a spectrum of progression rates and clinical sequelae that could be monitored in real time using 2-deoxy-2-[18F]fluoro-d-glucose (FDG) positron emission tomography (PET)/computed tomography (CT). Lesion pathology at sacrifice revealed the entire spectrum of lesions observed in human TB patients. The three strains produced different rates of progression to disease, various extents of extrapulmonary dissemination, and various degrees of cavitation. The majority of live births in this species are twins, and comparison of results from siblings with different infecting strains allowed us to establish that the infection was highly reproducible and that the differential virulence of strains was not simply host variation. Quantitative assessment of disease burden by FDG-PET/CT provided an accurate reflection of the pathology findings at necropsy. These results suggest that the marmoset offers an attractive small-animal model of human disease that recapitulates both the complex pathology and spectrum of disease observed in humans infected with various M. tuberculosis strain clades.

DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides

Antimicrobial peptides (AMPs) are anti-infectives that may represent a novel and untapped class of biotherapeutics. Increasing interest in AMPs means that new peptides (natural and synthetic) are discovered faster than ever before. We describe herein a new version of the Database of Antimicrobial Activity and Structure of Peptides (DBAASPv.2, which is freely accessible at http://dbaasp.org). This iteration of the database reports chemical structures and empirically-determined activities (MICs, IC50, etc.) against more than 4200 specific target microbes for more than 2000 ribosomal, 80 non-ribosomal and 5700 synthetic peptides. Of these, the vast majority are monomeric, but nearly 200 of these peptides are found as homo- or heterodimers. More than 6100 of the peptides are linear, but about 515 are cyclic and more than 1300 have other intra-chain covalent bonds. More than half of the entries in the database were added after the resource was initially described, which reflects the recent sharp uptick of interest in AMPs. New features of DBAASPv.2 include: (i) user-friendly utilities and reporting functions, (ii) a ‘Ranking Search’ function to query the database by target species and return a ranked list of peptides with activity against that target and (iii) structural descriptions of the peptides derived from empirical data or calculated by molecular dynamics (MD) simulations. The three-dimensional structural data are critical components for understanding structure–activity relationships and for design of new antimicrobial drugs. We created more than 300 high-throughput MD simulations specifically for inclusion in DBAASP. The resulting structures are described in the database by novel trajectory analysis plots and movies. Another 200+ DBAASP entries have links to the Protein DataBank. All of the structures are easily visualized directly in the web browser.

Whole-Genome Sequencing of Mycobacterium tuberculosis Provides Insight into the Evolution and Genetic Composition of Drug-Resistant Tuberculosis in Belarus

ABSTRACT The emergence and spread of drug-resistant Mycobacterium tuberculosis (DR-TB) are critical global health issues. Eastern Europe has some of the highest incidences of DR-TB, particularly multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. To better understand the genetic composition and evolution of MDR- and XDR-TB in the region, we sequenced and analyzed the genomes of 138 M. tuberculosis isolates from 97 patients sampled between 2010 and 2013 in Minsk, Belarus. MDR and XDR-TB isolates were significantly more likely to belong to the Beijing lineage than to the Euro-American lineage, and known resistance-conferring loci accounted for the majority of phenotypic resistance to first- and second-line drugs in MDR and XDR-TB. Using a phylogenomic approach, we estimated that the majority of MDR-TB was due to the recent transmission of already-resistant M. tuberculosis strains rather than repeated de novo evolution of resistance within patients, while XDR-TB was acquired through both routes. Longitudinal sampling of M. tuberculosis from 34 patients with treatment failure showed that most strains persisted genetically unchanged during treatment or acquired resistance to fluoroquinolones. HIV+ patients were significantly more likely to have multiple infections over time than HIV− patients, highlighting a specific need for careful infection control in these patients. These data provide a better understanding of the genomic composition, transmission, and evolution of MDR- and XDR-TB in Belarus and will enable improved diagnostics, treatment protocols, and prognostic decision-making.

The TB Portals: an Open-Access, Web-Based Platform for Global Drug-Resistant-Tuberculosis Data Sharing and Analysis

ABSTRACT The TB Portals program is an international consortium of physicians, radiologists, and microbiologists from countries with a heavy burden of drug-resistant tuberculosis working with data scientists and information technology professionals. Together, we have built the TB Portals, a repository of socioeconomic/geographic, clinical, laboratory, radiological, and genomic data from patient cases of drug-resistant tuberculosis backed by shareable, physical samples. Currently, there are 1,299 total cases from five country sites (Azerbaijan, Belarus, Moldova, Georgia, and Romania), 976 (75.1%) of which are multidrug or extensively drug resistant and 38.2%, 51.9%, and 36.3% of which contain X-ray, computed tomography (CT) scan, and genomic data, respectively. The top Mycobacterium tuberculosis lineages represented among collected samples are Beijing, T1, and H3, and single nucleotide polymorphisms (SNPs) that confer resistance to isoniazid, rifampin, ofloxacin, and moxifloxacin occur the most frequently. These data and samples have promoted drug discovery efforts and research into genomics and quantitative image analysis to improve diagnostics while also serving as a valuable resource for researchers and clinical providers. The TB Portals database and associated projects are continually growing, and we invite new partners and collaborations to our initiative. The TB Portals data and their associated analytical and statistical tools are freely available at https://tbportals.niaid.nih.gov/ .

Genome-wide Analysis of MDR and XDR Tuberculosis from Belarus: Machine-learning Approach

Emergence of drug-resistant microorganisms has been recognized as a serious threat to public health worldwide. This problem is extensively discussed in the context of tuberculosis treatment. Alterations in pathogen genomes are among the main mechanisms by which microorganisms exhibit drug resistance. Analysis of 144 M. tuberculosis strains of different phenotypes including drug susceptible, MDR, and XDR isolated in Belarus was fulfilled in this paper. A wide range of machine learning methods that can discover SNPs related to drug-resistance in the whole bacteria genomes was investigated. Besides single-SNP testing approaches, methods that allow detecting joint effects from interacting SNPs were considered. We proposed a framework for automated selection of the best performing statistical model in terms of recall, precision, and accuracy to identify drug resistance-associated mutations. Analysis of whole-genome sequences often leads to situations where the number of treated features exceeds the number of available observations. For this reason, special attention is paid to fair evaluation of the model prediction quality and minimizing the risk of overfitting while estimating the underlying parameters. Results of our experiments aimed at identifying top-scoring resistance mutations to the major first-line and second-line anti-TB drugs are presented.

Using biomarkers to predict TB treatment duration (Predict TB): a prospective, randomized, noninferiority, treatment shortening clinical trial

Background: By the early 1980s, tuberculosis treatment was shortened from 24 to 6 months, maintaining relapse rates of 1-2%. Subsequent trials attempting shorter durations have failed, with 4-month arms consistently having relapse rates of 15-20%. One trial shortened treatment only among those without baseline cavity on chest x-ray and whose month 2 sputum culture converted to negative. The 4-month arm relapse rate decreased to 7% but was still significantly worse than the 6-month arm (1.6%, P<0.01). We hypothesize that PET/CT characteristics at baseline, PET/CT changes at one month, and markers of residual bacterial load will identify patients with tuberculosis who can be cured with 4 months (16 weeks) of standard treatment. Methods: This is a prospective, multicenter, randomized, phase 2b, noninferiority clinical trial of pulmonary tuberculosis participants. Those eligible start standard of care treatment. PET/CT scans are done at weeks 0, 4, and 16 or 24. Participants who do not meet early treatment completion criteria (baseline radiologic severity, radiologic response at one month, and GeneXpert-detectable bacilli at four months) are placed in Arm A (24 weeks of standard therapy). Those who meet the early treatment completion criteria are randomized at week 16 to continue treatment to week 24 (Arm B) or complete treatment at week 16 (Arm C). The primary endpoint compares the treatment success rate at 18 months between Arms B and C. Discussion: Multiple biomarkers have been assessed to predict TB treatment outcomes. This study uses PET/CT scans and GeneXpert (Xpert) cycle threshold to risk stratify participants. PET/CT scans are not applicable to global public health but could be used in clinical trials to stratify participants and possibly become a surrogate endpoint. If the Predict TB trial is successful, other immunological biomarkers or transcriptional signatures that correlate with treatment outcome may be identified. Trial Registration: NCT02821832

Automatic Extraction and Categorization of Lung Abnormalities from HRCT Data in MDR/XDR TB Patients

An ancient disease, tuberculosis (TB) remains one of the major causes of disability and death worldwide. In 2006, 9.2 million new cases of TB emerged and killed 1.7 million people. We report on the development of tools to help in the detection of lesions and nodules from High Resolution Computed Tomography (HRCT) scans and changes in total lesion volumes across a study. These automated tools are designed to assist radiologists, clinicians and scientists assess patients’ responses to therapies during clinical studies. The tools are centered upon a rule-based system that initially segments the lung from HRCT scans and then categorizes the different components of the lung as normal or abnormal. A layered segmentation process, utilizing a combination of adaptive thresholding, three-dimensional region growing and component labeling is used to successively peel off outside entities, isolating lung and trachea voxels. Locating the Carina allows logical labeling of the trachea and left/right lungs. Shape and texture analysis are used to validate and label normal vascular tree voxels. Remaining abnormal voxels are clustered on density, gradient and texture-based criteria. Several practical problems that arise due to large changes in lung morphology due to TB and patients’ inability to hold their breath during scan operations need to be addressed to provide a viable computational solution. Comparisons of total common volumes of lesions by size for a given patient across multiple visits are in concordance with expert radiologist’s manual measurements.

Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria

Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods that try to predict AMPs. Most of them can only predict if a peptide will show any antimicrobial potency, but to the best of our knowledge, there are no tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMPs being active against particular Gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm. The algorithm can well distinguish peptides active against particular strains from others which may also be active but not against the considered strain. The available AMP prediction tools cannot carry out this task. The prediction tool based on the algorithm suggested herein is available on https://dbaasp.org.

Deploying Educational Roaming (eduroam) in a Rural Research Institution in Rakai, Uganda; Challenges and Lessons Learned

The NIAID International Center for Excellence in Research (ICER) in Uganda, RHSP (Rakai Health Sciences Program) recently deployed the eduroam service at the laboratories and offices in the village of Kalisizo and the main offices at Entebbe. Eduroam is a global framework to allow academics and researchers to have wireless access from any participating institution. An acronym for educational roaming, eduroam is a user friendly solution that provides a common WiFi network (SSID) at all participating universities and research organizations. Unlike the typical model of “guest” networks, this system provides a real identity to which network administrators and security staff can map both traffic and activity. There are clearly defined structures in place for reporting inappropriate activity to the home institution. The deployment of eduroam by the Office of Cyber Infrastructure and Computational Biology at the Ugandan ICER faced challenges and taught the team a number of lessons. The implementation began May 2016 in a test environment and was one of the first organizations to do so in Uganda. We share our experience in as far as challenges and lessons learnt.