Shantanu Sur

As emerging pathogens and antimicrobial resistance are increasing in prevalence, pathogen-agnostic, broad-spectrum therapeutics are urgently needed to address growing threats to public health. Here, we report a novel immune response-enhancing biomaterial that targets dendritic cells to activate innate immunity and strengthen the host defense to any potential pathogen. This biomaterial with therapeutic potential consists of a previously demonstrated dendritic cell-binding peptide (DC) conjugated to nanofibers formed by the self-assembling peptide, Q11. Antibody-based binding assays and computational modeling demonstrated that incorporation of DC-Q11 at 5% relative to unconjugated Q11 optimizes the surface presentation and allowed maximum accessibility of the DC peptide on these nanofibers for biological applications. DC-functionalized Q11 nanofibers promoted dendritic cell maturation, as evidenced by an increase in cell surface receptor expression profile, elevated proinflammatory cytokine production, and a decrease in anti-inflammatory cytokines. Further in vivo evaluation of this material construct could provide a more comprehensive understanding of its potential as a broad-spectrum therapeutic agent. Collectively, utilization of supramolecular materials platform, antibody engineering, and cellular assay techniques described herein might facilitate future research toward the development of broad-spectrum immunotherapeutic applications.

Nanopore sequencing can be advantageous over Sanger method for the identification of bioaerosol-derived bacterial colonies using 16S reads, enabling accurate detection of individual bacterial components with higher taxonomic resolution.

In a system of many similar self-propelled entities such as flocks of birds, fish school, cells and molecules, the interactions with neighbors can lead to a "coherent state", meaning the formation of visually compelling aggregation patterns due to the local adjustment of speed and direction. In this study, we explore one of the open questions that arise in studying collective patterns. When such entities, considered here as particles, tend to assume a coherent state beginning from an incoherent (random) state, what is the time interval for the transition? Also, how do model parameters affect this transition time interval? Given the observations of particle migration over a given time period as a point cloud data sampled at discrete time points, we use Topological Data Analysis, specifically persistent homology, to infer the transition time interval in which the particles undergo regime change. The topology of the particle configuration at any given time instance is captured by the persistent homology specifically Persistence Landscapes. We localize (in time) when such a transition happens by conducting the statistical significance tests namely functional hypothesis tests on persistent homology outputs corresponding to subsets of the time evolution. This process is validated on a known collective behavior model of the self-propelled particles with the regime transitions triggered by changing the model parameters in time. As an application, the developed technique was ultimately used to describe the transition in cellular movement from a disordered state to collective motion when the environment was altered.

Background: Rheumatoid arthritis (RA) is an autoimmune disorder characterized by chronic and systemic inflammation. Recent research underscores the role of chronic inflammation in multiple common RA comorbidities such as depression, obesity, and cardiovascular diseases (CVDs), suggesting a potential overlap of the pathogenic mechanisms for RA. However, it is not well understood how the coexistence of these comorbid conditions impacts the risk of RA and whether any such association relates to body's inflammatory state.Methods: We used data from the 2007-2010 United States National Health and Nutrition Examination Survey (NHANES) database and compared RA prevalence between subsamples with the presence of any two conditions among depression, obesity, and hypertriglyceridemia (HTG). Each subsample was further divided into three categories based on the serum level of the inflammatory marker C-reactive protein (CRP) and analyzed for statistically significant differences using three-way χ² tests of independence.Results: The study was conducted on 4,136 patients who fulfilled the inclusion criteria (representing 163,540,241 individuals after adjustment for sampling weights). Rates of depression, obesity, and HTG were found to be significantly higher (P < 0.001) among the subjects with RA compared with the control population with no arthritis. The presence of depression along with obesity or HTG showed a noticeably higher RA prevalence but such an association was not observed for the combination of obesity and HTG. The synergistic effect of HTG with depression was found to be most prominent at a medium CRP level (1–3 mg/L), while for obesity, the effect was observed across all CRP levels examined. These findings were further confirmed by the three-way χ² test for independence.Conclusions: The presence of obesity or HTG in subjects suffering from depression might pose an increased risk of RA. Inflammatory mechanisms potentially play an important underlying role as suggested by the strong dependency of the association to CRP level. Identification of synergistic associations between RA risk conditions could provide useful information to predict the development and progress of RA.

Microplastics (MPs) and nanoplastics (NPs) are hotspots for the exchange of antimicrobial resistance genes (ARGs) between different bacterial taxa in the environment. Propagation of antimicrobial resistance (AMR) is a global public health issue that needs special attention concerning horizontal gene transfer (HGT) under micro-nano plastics (MNPs) pressure. Interactions between MNPs and microbes, or mere persistence of MNPs in the environment (either water or soil), influence microbial gene expressions, affecting autochthonous microbiomes, their resistomes, and the overall ecosystem. The adsorption of a range of co-contaminants on MNPs leads to the increased interaction of pollutants with microbes resulting in changes in AMR, virulence, toxin production, etc. However, accurately estimating the extent of MNP infestation in agroecosystems remains challenging. The main limitation in estimating the level of MNPs contamination in agroecosystems, surface and subsurface waters, or sediments is the lack of standardized protocols for extraction of MPs and analytical detection methods from complex high organic content matrices. Nonetheless, recent advances in MPs detection from complex matrices with high organic matter content are highly promising. This review aims to provide an overview of relevant information available to date and summarize the already existing knowledge about the mechanisms of MNP-microbe interactions including the different factors with influence on HGT and AMR. In-depth knowledge of the enhanced ARGs propagation in the environment under the influence of MNPs could raise the needed awareness, about future consequences and emergence of multidrug-resistant bacteria.

The COVID-19 pandemic has generated a major interest in designing inhibitors to prevent SARS-CoV-2 binding on host cells to protect against infection. One promising approach to such research utilizes molecular dynamics simulation to identify potential inhibitors that can prevent the interaction between spike (S) protein on the virus and angiotensin converting enzyme 2 (ACE2) receptor on the host cells. In these studies, many groups have chosen to exclude the ACE2-bound zinc (Zn) ion, which is critical for its enzymatic activity. While the relatively distant location of Zn ion from the S protein binding site (S1 domain), combined with the difficulties in modeling this ion has motivated the decision of exclusion, Zn can potentially contribute to the structural stability of the entire protein, and thus, may have implications on S protein-ACE2 interaction. In this study, the authors model both the ACE2-S1 and ACE2-inhibitor (mAb) system to investigate if there are variations in structure and the readouts due to the presence of Zn ion. Although distant from the S1 or inhibitor binding region, inclusion/exclusion of Zn has statistically significant effects on the structural stability and binding free energy in these systems. In particular, the binding free energy of the ACE2-S1 and ACE2-inhibitor structures is − 3.26 and − 14.8 kcal/mol stronger, respectively, in the Zn-bound structure than in the Zn-free structures. This finding suggests that including Zn may be important in screening potentially inhibitors and may be particularly important in modeling monoclonal antibodies, which may be more sensitive to changes in antigen structure. Graphical abstract

PM2.5 has been linked to numerous pollution-mediated adverse health effects and their monitoring is key for taking preventative and mitigative measures. Accurate measurements of PM2.5 concentrations are available at EPA sites, but such data lacks spatial resolution due to a limited number of monitoring locations. In recent years the deployment of low-cost sensor networks has opened up the possibility of acquiring air quality data at a high spatiotemporal resolution. However, the sensitivity, noise, and accuracy of data acquired by low-cost sensors remain a concern. Here, we studied PM2.5 measurements made from EPA and Purple Air (PA) sensor networks in the Chicago area to understand the parameters influencing the performance characteristics of the low-cost sensor network. Using time series decomposition of PM2.5 data into short-term and baseline components using Kolmogorov–Zurbenko (KZ) filter and analysis of the extracted frequency signals, we determine that PA sensor data is more sensitive to meteorological conditions than anthropogenic activities in both short-term, and baseline components. We hypothesize that the low-cost sensor networks may have different sensitivity to aerosol from different sources and hence care must be taken in their calibrations and in their use for evaluating the impact of air quality mitigation policies.

Culture based detection remains to be one of the most reliable and acceptable techniques to detect extremely low quantity pathogens present in a sample. The process typically involves inoculating the sample on an agar plate to allow growth of the microorganisms to form colonies, followed by the identification of the individual colonies, commonly by DNA sequencing of a PCR-amplified targeted gene. Sanger method is often the default choice of sequencing as it offers affordable and accurate results for a single species. However, the technique could pose limitations in certain situations such as identification of multi-species microbial colonies. In this work, we compared the performance of Sanger sequencing with MinION nanopore sequencing in identifying bacterial colonies derived from bioaerosol samples. We conducted Sanger and nanopore sequencing of full-length 16S rRNA genes from seven bacterial colonies derived from bioaerosol samples and compared the outcome by alignment against NCBI 16S reference database. We found that for five out of seven colonies both techniques indicated the presence of the same bacterial genus. For one of the remaining colonies, a noisy Sanger electropherogram failed to generate a meaningful sequence, but nanopore sequencing identified it to be a mix of two bacterial genera Alkalihalobacillus and Kocuria . For the other remaining colony, the Sanger sequencing suggested Micrococcus with a clean electropherogram, however, the nanopore sequencing suggested the presence of an additional genus Paraburkholderia . Further corroborating these findings with mock multispecies colonies from pure bacterial DNA samples, we confirm that nanopore sequencing is comparable to the Sanger method in identifying colonies with single bacterial species but is the superior method in classifying individual bacterial components with their relative abundances in multispecies colonies. Our results suggest that nanopore sequencing could be advantageous over Sanger sequencing for colony identification in culture-based analysis of environmental samples such as bioaerosol where direct inoculation of the sample to culture plate might lead to formation of multispecies colonies.

Background: Rheumatoid arthritis (RA) is an autoimmune disorder characterized by chronic and systemic inflammation. Recent research underscores the role of chronic inflammation in multiple common RA comorbidities such as depression, obesity, and cardiovascular diseases (CVDs), suggesting a potential overlap of the pathogenic mechanisms for RA. However, it is not well understood how the coexistence of these comorbid conditions impacts the risk of RA and whether any such association relates to the inflammatory status of the body. Methods: We used data from the 2007-2010 United States National Health and Nutrition Examination Survey (NHANES) database and compared RA prevalence between subsamples with the presence of any two conditions among depression, obesity, and hypertriglyceridemia (HTG). Each subsample was further divided into three categories based on the serum level of the inflammatory marker C-reactive protein (CRP) and analyzed for statistically significant differences using three-way χ ² tests of independence. Results: The study was conducted on 4,136 patients who fulfilled the inclusion criteria (representing 163,540,241 individuals after adjustment for sampling weights). Rates of depression, obesity, and HTG were found to be significantly higher ( P < 0.001) among the subjects with RA compared with the control population with no arthritis. The presence of depression along with obesity or HTG showed a noticeably higher RA prevalence but such an association was not observed for the combination of obesity and HTG. The synergistic effect of HTG with depression was found to be most prominent at a medium CRP level (1-3 mg/L), while for obesity, the effect was observed across all CRP levels examined. These findings were further confirmed by the three-way χ ² test for independence. Conclusions: The presence of obesity or HTG in subjects suffering from depression might pose an increased risk of RA. Inflammatory mechanisms potentially play an important underlying role as suggested by the strong dependency of the association to CRP level. Identification of synergistic associations between RA risk conditions could provide useful information to predict the development and progress of RA.