Biology subjects

Chauhan, K.

Publications and source records attributed to Chauhan, K..

8 recordsLinked to original sources

bioRxiv2026

Production of a resilient biosurfactant using agro-wastes and Paenibacillus dendritiformis isolated from petroleum-contaminated soil: a break-even analysis at pilot scale

Wastes from mustard oil mills and dairy were used as partial replacement of pure reagents in modified Bushnell and Haas broth media for production of biosurfactant by Paenibacillus dendritiformis isolated from petroleum-contaminated soil. The process was optimised by construction of a response model using Taguchi experimental design, leading to a 28.23% increase in performance characteristics and a 13.23 % increase in yield of biosurfactant. The optimized production medium consisted of carbon @7.9 %, nitrogen @ 1.1% and agitation rate 180 r.p.m. This was provided by adding glucose @ 2%, ammonium nitrate @0.2%, mustard oil seed meal @10% and whey@ 10% in modified Bushnell Haas broth. The biosurfactant produced retained its properties during exposure to a wide range of pH values (5-11), high temperatures (up to 121C), high salinities of up to 5% and also five -fold concentration. Approximately three- fold increase in yield of the biosurfactant (25g/L to 68.5g/L) was obtained upon scaling-up the production from 100ml to 1500 ml in a lab scale fermenter with agitation @ 150r.p.m. and D.O. levels maintained at 6mg/L. Power calculations and the cost thereof was calculated (Rs 10.24 /L) and summed up with the cost of raw materials Rs 8.40/L and found to be Rs 18.64/L of product .Since 68.5g product is present in 1L, the cost is calculated to be Rs 271/- per kg. Comparison with selling price of currently used chemical surfactants like Linear Alkyl Benzene sulphonic acid (LAB) and sodium Lauryl sulphate and Sodium Lauryl ether sulphate (SLS and SLES) reveals that prices of these products varies from Rs 120 to 600 per kg depending on manufacturer and quality, order quantity. From the above studies it can be seen that production and use of this multiutility biosurfactant has, besides a clear advantage over chemically produced surfactants in terms of sustainability, a fairly good comparison with selling price of currently used chemical surfactants. The present study on the production costs (excluding capital costs) successfully demonstrates a break even analysis into the production of biosurfactants.

microbiology↗

bioRxiv2024

Scrutinized lipid utilization disrupts Amphotericin-B responsiveness in clinical isolates of Leishmania donovani

The management of Leishmania donovani (LD), responsible for fatal visceral leishmaniasis (VL), faces increasing challenges due to rising drug-unresponsiveness, leading to increasing treatment failures. While hypolipidemia characterizes VL, LD, a cholesterol auxotroph, relies on host lipid scavenging for its intracellular survival. The aggressive pathology, in terms of increased organ parasite load, observed in hosts infected with antimony-unresponsive-LD (LD-R) as compared to their sensitive counterparts (LD-S), highlights LD-Rs heightened reliance on host lipids. Here we report that LD-R-infection promotes fluid-phase endocytosis in the host, selectively accumulating neutral lipids while excluding oxidized-LDL. LD-R enhances the fusion of endocytosed LDL-vesicles with its phagolysosomal membrane and inhibits cholesterol mobilization from these vesicles by suppressing NPC-1. This provides LD-R amastigotes with excess lipids, supporting their rapid proliferation and membrane synthesis. This excess LDL-influx leads to an eventual accumulation of neutral lipid droplets around LD-R amastigotes, thereby increasing their unresponsiveness towards Amphotericin-B, a second-line amphiphilic antileishmanial. Notably, VL patients showing relapse with Amphotericin-B treatment exhibited significantly lower serum LDL and cholesterol than cured cases. Treatment with Aspirin, a lipid droplet blocker, reduced lipid droplets around LD-R amastigotes, restoring Amphotericin-B responsiveness.

microbiology↗

bioRxiv2024

A Mycobacterium tuberculosis secreted virulence factor disrupts host snRNP biogenesis

We earlier reported that Mycobacterium tuberculosis (Mtb) alters host RNA splicing to help its intracellular growth. Here, we report an unprecedented mechanism whereby a secreted virulence factor from Mtb interferes with the biogenesis of key spliceosomal components, causing an altered RNA splicing pattern. A high-throughput yeast-2-hybrid screen identified several Mtb-secreted proteins that can interact with the host RNA splicing factors (SFs). Through custom-designed in-cell assays, we show that one of those proteins, Rv1435c, targets specific exon-skipping events to alter RNA splicing. We show that Rv14345c or host splicing regulator 1 (hsr1) facilitates direct interaction between Mtb phagosomes and U5 snRNA and SNRPF, key components of the snRNPs. Genetic deletion of hsr1 reverses the specific exon-skipping events caused by WT Mtb infection. The{Delta} hsr1 strain shows compromised growth during ex vivo infection in the macrophages and in vivo infection in the mice. Tissue sections from the WT Mtb or{Delta} hsr1-infected mice showed significant hsr1-dependent SNRPF staining, a phenomenon also noted in the human intestinal tuberculosis (ITB) biopsies. We infer that hsr1 is a virulence factor which alters RNA splicing by interacting with U5snRNA and SNRPF. The splicing regulators from the host and pathogen are novel targets for anti-tuberculosis therapy.

molecular biology↗

bioRxiv2023

Bacterial deamidases modulate ubiquitin structure and dynamics to dysregulate ubiquitin signaling

The deamidases secreted by Burkholderia pseudomallei and Enteropathogenic Ecoli modify the residue 40 in ubiquitin from a Glutamine (Q) to Glutamate (E), triggering several downstream processes to cause cell cycle arrest and activate immune responses. Deamidation hampers the activity of ubiquitin and its interaction with ubiquitin chain receptors by an unknown mechanism. Here, we study the effect of deamidation on ubiquitin structure and dynamics. We report the crystal structure of the deamidated ubiquitin, supported by NMR and molecular dynamics simulations. The structure reveals a new intra-molecular salt bridge between the deamidated region and the C-terminal tail of ubiquitin. The salt bridge perturbs the dynamics of the ubiquitin tail to reduce affinity for ubiquitin receptors like the p62 ubiquitin-associated domain. The salt bridge disrupts the transition to catalytically active E2~Ub closed conformation. Consequently, RING E3s fail to interact with E2~Ub, reducing ubiquitination activity. Our findings reveal that deamidation-induced intramolecular salt bridges in ubiquitin modulate conformational ensembles to inactivate ubiquitination.

biochemistry↗

bioRxiv2021

The Prevalence of Litter Foraging Among UK Birds: Lessons Learned From A Citizen Science Project

1.Anthropogenic food sources, such as litter, are readily available to birds in urban areas. As an overly anthropogenic diet can have negative health consequences for wildlife, monitoring the frequency of foraging in litter compared to natural food resources can be an important component of wildlife conservation efforts. To understand how common litter foraging is among different bird species, we conducted a citizen science project with volunteers across the UK. Through this project, we also tried to engage people in birdwatching and bird surveys who would not normally participate in these activities. A particular focus was on recruiting respondents from underrepresented groups, and the methodology was designed to accommodate people without any background knowledge of birds. While we did not receive enough observations to draw conclusions about the litter foraging rates of different bird species across the UK, we report the submissions we received, as well as the demographic composition of the volunteer group. We successfully reached volunteers who did not participate in birdwatching or BTO bird surveys before, as well as some young volunteers, but were unsuccessful in reaching respondents from diverse socioeconomic backgrounds. We hope that the successes and failures of our methods reported here can be useful to others designing citizen science studies, so that birdwatching and volunteering for bird surveys will be equally accessible to all in the future.

ecology↗

bioRxiv2019

Prediction of rapid kidney function decline using machine learning combining blood biomarkers and electronic health record data

IntroductionIndividuals with type 2 diabetes (T2DM) or the APOL1 high-risk genotype (APOL1) are at increased risk of rapid kidney function decline (RKFD) as compared to the general population. Plasma biomarkers representing inflammatory and kidney injury pathways have been validated as predictive of kidney disease progression in several studies. In addition, routine clinical data in the electronic health record (EHR) may also be utilized for predictive purposes. The application of machine learning to integrate biomarkers with clinical data may lead to improved identification of RKFD.\n\nMethodsWe selected two subpopulations of high-risk individuals: T2DM (n=871) and APOL1 high risk genotype of African Ancestry (n=498), with a baseline eGFR [≥] 45 ml/min/1.73 m2 from the Mount Sinai BioMe Biobank. Plasma levels of tumor necrosis factor 1/2 (TNFR1/2), and kidney injury molecule-1 (KIM-1) were measured and a series of supervised machine learning approaches including random forest (RF) were employed to combine the biomarker data with longitudinal clinical variables. The primary objective was to accurately predict RKFD (eGFR decline of [≥] 5 ml/min/1.73 m2/year) based on an algorithm-produced score and probability cutoffs, with results compared to standard of care.\n\nResultsIn 871 participants with T2DM, the mean age was 61 years, baseline estimated glomerular filtration rate (eGFR) was 74 ml/min/1.73 m2, and median UACR was 13 mg/g. The median follow-up was 4.7 years from the baseline specimen collection with additional retrospective data available for a median of 2.3 years prior to plasma collection. In the 498 African Ancestry patients with high-risk APOL1 genotype, the median age was 56 years, median baseline eGFR was 83 ml/min/1.73 m2,and median UACR was 11 mg/g. The median follow-up was 4.7 years and there was additional retrospective data available for 3.1 years prior to plasma collection. Overall, 19% with T2DM, and 9% of the APOL1 high-risk genotype experienced RKFD. After evaluation of three supervised algorithms: random forest (RF), support vector machine (SVM), and Cox survival, the RF model was selected. In the training and test sets respectively, the RF model had an AUC of 0.82 (95% CI, 0.81-0.83) and 0.80 (95% CI, 0.78-0.82) in T2DM, and an AUC of 0.85 (95% CI, 0.84-0.87) and 0.80 (95% CI, 0.73-0.86) for the APOL1 high-risk group. The combined RF model outperformed standard clinical variables in both patient populations. Discrimination was comparable in two sensitivity analyses: 1) Using only data from [≤] 1 year prior to baseline biomarker measurement and 2) In individuals with eGFR [≤]60 and/or albuminuria at baseline. The distribution of RFKD probability varied in the two populations. In patients with T2DM, the RKFD score stratified 18%, 49%, and 33% of patients to high-, intermediate-, and low-probability strata, respectively, with a PPV of 53% in the high-probability group and an NPV of 97% in the low-probability group. By comparison, in the APOL1 high-risk genotype, the RKFD score stratified 7%, 23%, and 70% of patients to high-, intermediate-, and low-probability strata, respectively, with a 46% PPV in the high-probability and an NPV of 98% NPV in the low-probability group.\n\nConclusionsIn patients with T2DM or of African Ancestry with the high-risk APOL1 genotype, a RF model derived from plasma biomarkers and longitudinal EHR data significantly improved prediction of rapid kidney function decline over standard clinical models. With further validation, this approach may be valuable in aiding clinicians in identifying patients who would benefit most from early and more aggressive follow-up to mitigate kidney disease progression.

epidemiology↗

bioRxiv2018

Comparison of Approaches to the identification of Symptom Burden in Hemodialysis Patients Utilizing Electronic Health Records

BackgroundIdentification of symptoms is challenging with surveys, which are time-intensive and low-throughput. Natural language processing (NLP) could be utilized to identify symptoms from narrative documentation in the electronic health record (EHR).\n\nMethodsWe utilized NLP to parse notes for maintenance hemodialysis (HD) patients from two EHR databases (BioMe and MIMIC-III) to identify fatigue, nausea/vomiting, anxiety, depression, cramping, itching, and pain. We compared NLP performance with International Classification of Diseases (ICD) codes and validated the performance of both NLP and codes against manual chart review in a representative subset.\n\nResultsWe identified 1034 and 929 HD patients from BioMe and MIMIC-III respectively. The most frequently identified symptoms by NLP from both cohorts were fatigue, pain, and nausea and/or vomiting. NLP was significantly more sensitive than ICD codes for nearly all symptoms. In the BioMe dataset, sensitivity for NLP ranged from 0.85-0.99 vs. 0.09-0.59 for ICD codes. In the MIMIC-III dataset, NLP sensitivity was 0.8-0.98 vs. 0.02-0.53 for ICD. ICD codes were significantly more specific for nausea and/or vomiting (NLP 0.57 vs. ICD 0.97, P=0.03) in BioMe and for depression (NLP 0.67 vs. ICD 0.99, P=0.002) in MIMIC-III. A majority of patients in both cohorts had ?4 symptoms. The more encounters available for a patient the more likely NLP was to identify a symptom.\n\nConclusionsNLP out performed ICD codes for identification of symptoms on several tests parameters including sensitivity for a majority of symptoms. NLP may be useful for the high-throughput identification of patient centered outcomes from EHR.\n\nSignificance StatementPatients on maintenance hemodialysis experience a high frequency of symptoms. However, symptoms have been measured utilizing time-intensive surveys. This paper compares natural language processing (NLP) to administrative codes for the identification of seven key symptoms from two cohorts with electronic health records and validation through manual chart review. NLP identified high rates of symptoms; the most common were fatigue, pain, and nausea and/or vomiting. A majority of patients had [≥]4 symptoms. NLP was significantly more sensitive at identifying symptoms compared to administrative codes for nearly all symptoms but specificity was not significantly different compared to codes. This paper demonstrates utility of a high throughput method of identifying symptoms from EHR which may advance the field of patient centered research in nephrology.

epidemiology↗

bioRxiv2018

Unsupervised Machine learning to subtype Sepsis-Associated Acute Kidney Injury

ObjectiveAcute kidney injury (AKI) is highly prevalent in critically ill patients with sepsis. Sepsis-associated AKI is a heterogeneous clinical entity, and, like many complex syndromes, is composed of distinct subtypes. We aimed to agnostically identify AKI subphenotypes using machine learning techniques and routinely collected data in electronic health records (EHRs).\n\nDesignCohort study utilizing the MIMIC-III Database.\n\nSettingICUs from tertiary care hospital in the U.S.\n\nPatientsPatients older than 18 years with sepsis and who developed AKI within 48 hours of ICU admission.\n\nInterventionsUnsupervised machine learning utilizing all available vital signs and laboratory measurements.\n\nMeasurements and Main ResultsWe identified 1,865 patients with sepsis-associated AKI. Ten vital signs and 691 unique laboratory results were identified. After data processing and feature selection, 59 features, of which 28 were measures of intra-patient variability, remained for inclusion into an unsupervised machine-learning algorithm. We utilized k-means clustering with k ranging from 2 - 10; k=2 had the highest silhouette score (0.62). Cluster 1 had 1,358 patients while Cluster 2 had 507 patients. There were no significant differences between clusters on age, race or gender. We found significant differences in comorbidities and small but significant differences in several laboratory variables (hematocrit, bicarbonate, albumin) and vital signs (systolic blood pressure and heart rate). In-hospital mortality was higher in cluster 2 patients, 25% vs. 20%, p=0.008. Features with the largest differences between clusters included variability in basophil and eosinophil counts, alanine aminotransferase levels and creatine kinase values.\n\nConclusionsUtilizing routinely collected laboratory variables and vital signs in the EHR, we were able to identify two distinct subphenotypes of sepsis-associated AKI with different outcomes. Variability in laboratory variables, as opposed to their actual value, was more important for determination of subphenotypes. Our findings show the potential utility of unsupervised machine learning to better subtype AKI.

bioinformatics↗