Drug-Resistant Tuberculosis, Georgia, Kazakhstan, Kyrgyzstan, Moldova, and Ukraine, 2017-2022.

In 2021, the World Health Organization recommended new extensively drug-resistant (XDR) and pre-XDR tuberculosis (TB) definitions. In a recent cohort of TB patients in Eastern Europe, we show that XDR TB as currently defined is associated with exceptionally poor treatment outcomes, considerably worse than for the former definition (31% vs. 54% treatment success).

Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction.

The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular MS. Due to insufficient data on most MS, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target MS. New models can perform predictions of AMPs for MS that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased relative to models relying on AMP sequence-based properties only. Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at http://dbaasp.org/prediction/genome.

Benchmarking the empirical accuracy of short-read sequencing across the M. tuberculosis genome.

MOTIVATION: Short-read whole-genome sequencing (WGS) is a vital tool for clinical applications and basic research. Genetic divergence from the reference genome, repetitive sequences and sequencing bias reduces the performance of variant calling using short-read alignment, but the loss in recall and specificity has not been adequately characterized. To benchmark short-read variant calling, we used 36 diverse clinical Mycobacterium tuberculosis (Mtb) isolates dually sequenced with Illumina short-reads and PacBio long-reads. We systematically studied the short-read variant calling accuracy and the influence of sequence uniqueness, reference bias and GC content. RESULTS: Reference-based Illumina variant calling demonstrated a maximum recall of 89.0% and minimum precision of 98.5% across parameters evaluated. The approach that maximized variant recall while still maintaining high precision (<99%) was tuning the mapping quality filtering threshold, i.e. confidence of the read mapping (recall = 85.8%, precision = 99.1%, MQ ≥ 40). Additional masking of repetitive sequence content is an alternative conservative approach to variant calling that increases precision at cost to recall (recall = 70.2%, precision = 99.6%, MQ ≥ 40). Of the genomic positions typically excluded for Mtb, 68% are accurately called using Illumina WGS including 52/168 PE/PPE genes (34.5%). From these results, we present a refined list of low confidence regions across the Mtb genome, which we found to frequently overlap with regions with structural variation, low sequence uniqueness and low sequencing coverage. Our benchmarking results have broad implications for the use of WGS in the study of Mtb biology, inference of transmission in public health surveillance systems and more generally for WGS applications in other organisms. AVAILABILITY AND IMPLEMENTATION: All relevant code is available at https://github.com/farhat-lab/mtb-illumina-wgs-evaluation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Financial toxicity in BRCA1 and BRCA2 carriers.

OBJECTIVE: Financial toxicity (FT), the cumulative financial burden experienced due to medical care, is a well-established adverse effect of healthcare. Patients with BRCA mutations have significantly increased cancer risks compared to non-affected individuals, requiring more frequent screenings and, at times, prophylactic surgery, increasing their risk for FT. Our primary aim in this study was to describe rates of FT among BRCA carriers. METHODS: We performed a novel, cross-sectional study of FT in BRCA1/2 carriers. Participants were recruited via phone and/or email to complete consents and surveys on REDCap. The FACIT-COST tool, a validated tool for measuring FT, was used to assess FT; scores were divided into tertiles, with high FT defined as COST score < 24. RESULTS: 265 BRCA positive female participants met enrollment criteria; 76 (28.7%) consented to participate and completed the survey. Participants were primarily non-Hispanic White (97.4%), privately insured (82.9%), and employed full time (67.1%). A significant proportion (22.7%) of participants reported delaying or avoiding care secondary to finances. No statistically significant association was seen between financial toxicity groups and analyzed demographics. Participants with high FT were more likely to engage in all surveyed cost-saving measures, with 41.7% of participants reporting delays/avoidance of care due to cost (p = 0.02). CONCLUSIONS: This study of FT in BRCA carriers shows that financial toxicity exists as an issue in this high-risk patient population. This work serves as the first description of FT in BRCA mutation carriers and highlights the importance of incorporating routine counseling on cost when discussing recommendations for screening and clinical care with this patient population.

DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics.

The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.

Acceptability of HIV Prevention Approaches Among USA Faith-Based Leaders.

HIV/AIDS disproportionately impacts underserved communities in the USA. This study evaluated the acceptability of partnering with faith communities to improve HIV prevention, screening, and engagement in care with a focus on Hispanic/Latinx (H/L) communities. We engaged faith-based leaders to identify how the cultural competence and foundations of trust within these communities may be leveraged to improve HIV-related healthcare access for underserved groups including H/L individuals. Using a semi-structured qualitative interview approach, we interviewed N = 20 faith-based leaders in Providence County, Rhode Island (RI). Data were analyzed using the framework approach which utilized inductive generation of themes and systematic grouping into predetermined categories. Seven of the 20 interviewees self-identified as H/L faith leaders and discussed needs specific to H/L communities including destigmatization of HIV, increased access to care, and partnerships founded on mutual respect. The other 13 faith leaders did not personally identify as H/L but all served communities with significant H/L populations. We included these individuals given their communities already performed HIV and/or other health outreach and could provide insight into what approaches could be adapted to the needs expressed by H/L leaders. All interviewees were accepting of developing partnerships with outside organizations to engage in HIV prevention, and all identified potential solutions to identified barriers. Results suggested that faith-based outreach should be further investigated as a method of improving HIV prevention in the general and H/L populations.

A retrospective genomic analysis of drug-resistant strains of M. tuberculosis in a high-burden setting, with an emphasis on comparative diagnostics and reactivation and reinfection status.

BACKGROUND: Recurrence of drug-resistant tuberculosis (DR-TB) after treatment occurs through relapse of the initial infection or reinfection by a new drug-resistant strain. Outbreaks of DR-TB in high burden regions present unique challenges in determining recurrence status for effective disease management and treatment. In the Republic of Moldova the burden of DR-TB is exceptionally high, with many cases presenting as recurrent. METHODS: We performed a retrospective analysis of Mycobacterium tuberculosis from Moldova to better understand the genomic basis of drug resistance and its effect on the determination of recurrence status in a high DR-burden environment. To do this we analyzed genomes from 278 isolates collected from 189 patients, including 87 patients with longitudinal samples. These pathogen genomes were sequenced using Illumina technology, and SNP panels were generated for each sample for use in phylogenetic and network analysis. Discordance between genomic resistance profiles and clinical drug-resistance test results was examined in detail to assess the possibility of mixed infection. RESULTS: There were clusters of multiple patients with 10 or fewer differences among DR-TB samples, which is evidence of person-to-person transmission of DR-TB. Analysis of longitudinally collected isolates revealed that many infections exhibited little change over time, though 35 patients demonstrated reinfection by divergent (number of differences > 10) lineages. Additionally, several same-lineage sample pairs were found to be more divergent than expected for a relapsed infection. Network analysis of the H3/4.2.1 clade found very close relationships among 61 of these samples, making differentiation of reactivation and reinfection difficult. There was discordance between genomic profile and clinical drug sensitivity test results in twelve samples, and four of these had low level (but not statistically significant) variation at DR SNPs suggesting low-level mixed infections. CONCLUSIONS: Whole-genome sequencing provided a detailed view of the genealogical structure of the DR-TB epidemic in Moldova, showing that reinfection may be more prevalent than currently recognized. We also found increased evidence of mixed infection, which could be more robustly characterized with deeper levels of genomic sequencing.

Pushing the limits of solids retention time for enhanced biological phosphorus removal: process characteristics and Accumulibacter population structure.

Reducing the solids retention time (SRT) of the enhanced biological phosphorus removal (EBPR) process can increase organic carbon diversion to the sidestream for energy recovery, thereby realizing some of the benefits of the high rate activated sludge (HRAS) process. Determining the washout (i.e. minimum) SRT of polyphosphate accumulating organisms (PAOs), therefore, allows for simultaneous phosphorus and carbon diversion for energy recovery from EBPR systems. However, few studies have investigated the washout SRT of PAOs in real wastewater, and little is known of the diversity of PAOs in high rate EBPR systems. Here we demonstrate efficient phosphorus removal (83% orthophosphate removal) in a high rate EBPR sequencing batch reactor fed real primary effluent and operated at 20 °C. Stable operation was achieved at a total SRT of 1.8 ± 0.2 days and hydraulic retention time of 3.7-4.8 hours. 16S rRNA gene sequencing data demonstrated that Accumulibacter were the dominant PAO throughout the study, with a washout aerobic SRT between 0.8 and 1.4 days. qPCR targeting the polyphosphate kinase gene revealed that Accumulibacter clades IIA, IIB and IID dominated the PAO community at low SRT operation, while clade IA was washed out at the lowest SRT values.

Spatially resolved abundances of antibiotic resistance genes and intI1 in wastewater treatment biofilms.

Attached growth bioprocesses that use biofilms to remove organic matter or nutrients from wastewater are known to harbor antibiotic resistance genes (ARGs). Biofilms in these processes are spatially heterogeneous, but little is known about depth stratification of ARGs in complex, mixed culture biofilms. To address this knowledge gap, we used an experimental approach combining cryosectioning and quantitative polymerase chain reaction to quantify the spatial distribution of three ARGs (sul1, ermB, and qnrS) and the class 1 integron-integrase gene intI1 in biofilms from a lab-scale rotating annular reactor fed with synthetic wastewater. We also used high throughput 16S ribosomal RNA (rRNA) gene sequencing to characterize community structure with depth in biofilms. The ARG sul1 and the integron-integrase gene intI1 were found in higher abundances in upper layers of biofilm near the fluid-biofilm interface than in lower layers and exhibited significant correlations between the distance from substratum and gene abundances. The genes ermB and qnrS were present in comparatively low relative abundances. Microbial community structure varied significantly by date of sampling and distance from the substratum. These findings highlight the genetic and taxonomic heterogeneity with distance from substratum in wastewater treatment biofilms and show that sul1 and intI1 are particularly abundant near fluid-biofilm interfaces where cells are most likely to detach and flow into downstream portions of treatment systems and can ultimately be released into the environment through effluent.

Towards mechanical characterization of granular biofilms by optical coherence elastography measurements of circumferential elastic waves.

Microbial granular biofilms are spherical, multi-layered aggregates composed of communities of bacterial cells encased in a complex matrix of hydrated extracellular polymeric substances (EPS). While granular aggregates are increasingly used for applications in industrial and municipal wastewater treatment, their underlying mechanical properties are poorly understood. The challenges of viscoelastic characterization for these structures are due to their spherical geometry, spatially heterogeneous properties, and their delicate nature. In this study, we report a model-based approach for nondestructive characterization of viscoelastic properties (shear modulus and shear viscosity) of alginate spheres with different concentrations, which was motivated by our measurements in granular biofilms. The characterization technique relies on experimental measurements of circumferential elastic wave speeds as a function of frequency in the samples using the Optical Coherence Elastography (OCE) technique. A theoretical model was developed to estimate the viscoelastic properties of the samples from OCE data through inverse analysis. This work represents the first attempt to explore elastic waves for mechanical characterization of granular biofilms. The combination of the OCE technique and the theoretical model presented in this paper provides a framework that can facilitate quantitative viscoelastic characterization of samples with curved geometries and the study of the relationships between morphology and mechanical properties in granular biofilms.

Morphological analysis of pore size and connectivity in a thick mixed-culture biofilm.

Morphological parameters are commonly used to predict transport and metabolic kinetics in biofilms. Yet, quantification of biofilm morphology remains challenging because of imaging technology limitations and lack of robust analytical approaches. We present a novel set of imaging and image analysis techniques to estimate internal porosity, pore size distributions, and pore network connectivity to a depth of 1 mm at a resolution of 10 µm in a biofilm exhibiting both heterotrophic and nitrifying activities. Optical coherence tomography (OCT) scans revealed an extensive pore network with diameters as large as 110 µm directly connected to the biofilm surface and surrounding fluid. Thin-section fluorescence in situ hybridization microscopy revealed that ammonia-oxidizing bacteria (AOB) distributed through the entire thickness of the biofilm. AOB were particularly concentrated in the biofilm around internal pores. Areal porosity values estimated from OCT scans were consistently lower than those estimated from multiphoton laser scanning microscopy, though the two imaging modalities showed a statistically significant correlation (r = 0.49, p < 0.0001). Estimates of areal porosity were moderately sensitive to gray-level threshold selection, though several automated thresholding algorithms yielded similar values to those obtained by manually thresholding performed by a panel of environmental engineering researchers (±25% relative error). These findings advance our ability to quantitatively describe the geometry of biofilm internal pore networks at length scales relevant to engineered biofilm reactors and suggest that internal pore structures provide crucial habitat for nitrifier growth.

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.

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.

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

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/.

Tuberculosis Chest X-Ray Image Retrieval System Using Deep Learning Based Biomarker Predictions.

The world health organization's global tuberculosis (TB) report for 2022 identifies TB, with an estimated 1.6 million, as a leading cause of death. The number of new cases has risen since 2020, particularly the number of new drug-resistant cases, estimated at 450,000 in 2021. This is concerning, as treatment of patients with drug resistant TB is complex and may not always be successful. The NIAID TB Portals program is an international consortium with a primary focus on patient centric data collection and analysis for drug resistant TB. The data includes images, their associated radiological findings, clinical records, and socioeconomic information. This work describes a TB Portals' Chest X-ray based image retrieval system which enables precision medicine. An input image is used to retrieve similar images and the associated patient specific information, thus facilitating inspection of outcomes and treatment regimens from comparable patients. Image similarity is defined using clinically relevant biomarkers: gender, age, body mass index (BMI), and the percentage of lung affected per sextant. The biomarkers are predicted using variations of the DenseNet169 convolutional neural network. A multi-task approach is used to predict gender, age and BMI incorporating transfer learning from an initial training on the NIH Clinical Center CXR dataset to the TB portals dataset. The resulting gender AUC, age and BMI mean absolute errors were 0.9854, 4.03years and 1.67kgm2. For the percentage of sextant affected by lesions the mean absolute errors ranged between 7% to 12% with higher error values in the middle and upper sextants which exhibit more variability than the lower sextants. The retrieval system is currently available from https://rap.tbportals.niaid.nih.gov/find_similar_cxr.

Evaluation of aircraft deicing fluid as an external carbon source for denitrification.

Water resource recovery facilities (WRRFs) performing biological nitrogen removal (BNR) often require external carbon sources for meeting nitrogen discharge permit limits. This brings an additional financial burden to the facilities considering the continuous need of these external carbon sources. This paper evaluates the utilization of airport stormwater, which in the winter season is rich in aircraft deicing fluid (ADF) as an alternative external carbon source. Denitrification and nitrification bench scale experiments were performed to assess the efficacy of external carbon sources to remove nitrogen in WRRFs. Experimental results showed that ADFs achieve denitrification rates of 0.064-0.066 d-1, higher than what achieved by a commercial carbon source, MicroC 2000A, with corresponding value of 0.058 d-1 at low temperatures, as low as 13 °C, which is considered a worst-case scenario for nitrogen removal efficiency. Furthermore, no inhibition to nitrification associated with the ADFs was observed. Subsequently a dynamic modeling study was conducted to assess the performance of ADFs as external carbon sources for denitrification and compared them to the conventional source that was being used in a full-scale BNR process. Results from the dynamic modeling study revealed that if 40 % of the spent-ADF at LaGuardia airport, New York City, could be collected with the stormwater and conveyed to a WRRF via the sewer collection system, an approximate reduction of 30 % of the commercial external carbon source could be accomplished by repurposing a waste product. This study contributes to the potential of ADF as a denitrification aid and an alternative for commercially available carbon sources with comparable nitrogen removal efficiencies.

Left-Handed Palming of the Needle Driver: A Technical Report for Surgical Trainees.

Left-handed surgical trainees are uniquely challenged when learning how to suture using standard needle drivers designed for right-handed individuals and often feel disadvantaged in comparison to their right-handed peers. "Palming," a suturing technique that improves suturing mechanics and efficiency, cannot be achieved in the standard manner using the left hand. This paper proposes a previously undescribed technique for palming using the left hand that provides many of the same benefits as standard palming methods using the right hand, potentially reducing a common source of inequity in surgical training.

Evaluation of the synergistic potential and mechanisms of action for de novo designed cationic antimicrobial peptides.

Antimicrobial peptides (AMPs) have emerged as promising candidates in combating antimicrobial resistance - a growing issue in healthcare. However, to develop AMPs into effective therapeutics, a thorough analysis and extensive investigations are essential. In this study, we employed an in silico approach to design cationic AMPs de novo, followed by their experimental testing. The antibacterial potential of de novo designed cationic AMPs, along with their synergistic properties in combination with conventional antibiotics was examined. Furthermore, the effects of bacterial inoculum density and metabolic state on the antibacterial activity of AMPs were evaluated. Finally, the impact of several potent AMPs on E. coli cell envelope and genomic DNA integrity was determined. Collectively, this comprehensive analysis provides insights into the unique characteristics of cationic AMPs.

Differences in pulmonary nodular consolidation and pulmonary cavity among drug-sensitive, rifampicin-resistant and multi-drug resistant tuberculosis patients: the Guangzhou computerized tomography study.

Background: Pulmonary nodular consolidation (PN) and pulmonary cavity (PC) may represent the two most promising imaging signs in differentiating multidrug-resistant (MDR)-pulmonary tuberculosis (PTB) from drug-sensitive (DS)-PTB. However, there have been concerns that literature described radiological feature differences between DS-PTB and MDR-PTB were confounded by that MDR-PTB cases tend to have a longer history. This study seeks to further clarify this point. Methods: All cases were from the Guangzhou Chest Hospital, Guangzhou, China. We retrieved data of consecutive new MDR cases [n=46, inclusive of rifampicin-resistant (RR) cases] treated during the period of July 2020 and December 2021, and according to the electronic case archiving system records, the main PTB-related symptoms/signs history was ≤3 months till the first computed tomography (CT) scan in Guangzhou Chest Hospital was taken. To pair the MDR-PTB cases with assumed equal disease history length, we additionally retrieved data of 46 cases of DS-PTB patients. Twenty-two of the DS patients and 30 of the MDR patients were from rural communities. The first CT in Guangzhou Chest Hospital was analysed in this study. When the CT was taken, most cases had anti-TB drug treatment for less than 2 weeks, and none had been treated for more than 3 weeks. Results: Apparent CT signs associated with chronicity were noted in 10 cases in the DS group (10/46) and 9 cases in the MDR group (10/46). Thus, the overall disease history would have been longer than the assumed <3 months. Still, the history length difference between DS patients and MDR patients in the current study might not be substantial. The lung volume involvement was 11.3%±8.3% for DS cases and 8.4%±6.6% for MDR cases (P=0.022). There was no statistical difference between DS cases and MDR cases both in PN prevalence and in PC prevalence. For positive cases, MDR cases had more PN number (mean of positive cases: 2.63 vs. 2.28, P=0.38) and PC number (mean of positive cases: 2.14 vs. 1.38, P=0.001) than DS cases. Receiver operating characteristic curve analysis shows, PN ≥4 and PC ≥3 had a specificity of 86% (sensitivity 25%) and 93% (sensitivity 36%), respectively, in suggesting the patient being a MDR cases. Conclusions: A combination of PN and PC features allows statistical separation of DS and MDR cases.

Analysis of the limited M. tuberculosis accessory genome reveals potential pitfalls of pan-genome analysis approaches.

Pan-genome analysis is a fundamental tool for studying bacterial genome evolution; however, the variety of methods used to define and measure the pan-genome poses challenges to the interpretation and reliability of results. To quantify sources of bias and error related to common pan-genome analysis approaches, we evaluated different approaches applied to curated collection of 151 Mycobacterium tuberculosis ( Mtb ) isolates. Mtb is characterized by its clonal evolution, absence of horizontal gene transfer, and limited accessory genome, making it an ideal test case for this study. Using a state-of-the-art graph-genome approach, we found that a majority of the structural variation observed in Mtb originates from rearrangement, deletion, and duplication of redundant nucleotide sequences. In contrast, we found that pan-genome analyses that focus on comparison of coding sequences (at the amino acid level) can yield surprisingly variable results, driven by differences in assembly quality and the softwares used. Upon closer inspection, we found that coding sequence annotation discrepancies were a major contributor to inflated Mtb accessory genome estimates. To address this, we developed panqc, a software that detects annotation discrepancies and collapses nucleotide redundancy in pan-genome estimates. When applied to Mtb and E. coli pan-genomes, panqc exposed distinct biases influenced by the genomic diversity of the population studied. Our findings underscore the need for careful methodological selection and quality control to accurately map the evolutionary dynamics of a bacterial species.