Biology subjects

Saini, G.

Publications and source records attributed to Saini, G..

7 recordsLinked to original sources

bioRxiv2024

Genetic burden of dysregulated cytoskeletal organisation in the pathogenesis of pulmonary fibrosis

BackgroundRare genetic variants contribute to pulmonary fibrosis (PF) risk and outcome, with known variants highlighting the importance of impaired telomere maintenance and surfactant biology. However, much of the disrupted genetic architecture of PF remains unexplained. This study aimed to identify genes with rare pathogenic coding variants that represented a burden at the exon level associated with the pathogenesis of PF. MethodsPF case cohorts included the PROFILE study of incident idiopathic pulmonary fibrosis (IPF) and PF-classified participants from the Genomics England 100K (GE100KGP) study. Whole genome sequencing data were used to test the burden of rare protein altering variants (PAVs; CADD score >20) defined by minor allele frequency <0.1% in the summary level gnomAD reference database (v3.1.2). A summary exon-level pathogenicity score was derived by standardising PAV predictions from functional annotation tools (AlphaMissense, REVEL, ClinPred, CADD, PolyPhen-2, SIFT) averaged across exons. SKAT-O based kernel regression associated exon-level pathogenicity features with disease progression and survival. Single cell lung transcriptomic datasets were harmonised to evaluate expression patterns and gene ontology characterised enriched molecular functions. ResultsAcross 507 PROFILE cases and 451 GE100KGP cases, each compared against 76,156 reference participants, 77 genes showed overlapping significant rare PAV burden, comprising 206 concordant PAVs. Of these, 15 genes had exons with pathogenicity scores that were associated with worse clinical outcomes in IPF, including FAT4 exon-10 and DNAH7 exon-43. Dysregulated gene expression of COL6A3 and FAT4 was observed in IPF lung fibroblasts, while DNAH7, DNAH12 and PCM1 showed high expression in IPF epithelial cells. The top enriched molecular functions were related to cytoskeletal motor activity. ConclusionsRare PAV burden highlighted that pathogenicity within specific exons was associated with PF risk and worse clinical outcomes, identifying genes with dysregulated expression in disease lung cells. The genetic architecture implicates disrupted cytoskeletal organisation in the pathogenesis of PF.

genetics↗

bioRxiv2023

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning

BackgroundNeoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30%-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. MethodsSerial sections from core needle biopsies (n=76) were stained with H&E, and immunohistochemically for the Ki67 and pH3 markers, followed by whole slide image (WSI) generation. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models, and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. ResultsHighest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. ConclusionsOverall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

cancer biology↗

bioRxiv2022

A unified peptide array platform for antibody epitope binning, mapping, specificity and predictive off-target binding

Therapeutic antibody efficacy is largely determined by the target epitope. In addition, off-target binding can result in unanticipated side-effects. Therefore, characterization of the epitope and binding specificity are critical in antibody discovery. Epitope binning provides low-resolution of an antibody epitope and is typically performed as a cross-blocking assay to group antibodies into overlapping or non-overlapping bins. Epitope mapping identifies the epitope with high resolution but requires low throughput methods. In addition to binning and mapping, there is a need for a scalable and predictive approach to reveal off-target binding early in antibody discovery to reduce the risk of in vivo side effects. Peptide microarrays are an information-rich platform for antibody characterization. However, the potential of peptide microarrays in early-stage antibody discovery has not been realized because they are not produced at the scale, quality and format needed for reliable high-throughput antibody characterization. A unified, peptide library platform for high-resolution antibody epitope binning, mapping and predictive off-target binding characterization is described here. This platform uses highly scalable array synthesis and photolithography to synthesize more than 3 million addressable peptides. These arrays conform to a microplate format and each synthesis is qualified with mass spectrometry. Using this platform, a scalable approach to early-stage epitope and specificity characterization, with prediction of off-target interaction(s), is demonstrated using a panel of anti-HER2 monoclonal antibodies. This study highlights the prospect of this platform to improve antibody discovery productivity by generating epitope and specificity information much earlier with potentially hundreds of antibody clones.

bioengineering↗

bioRxiv2021

A lung targeted miR-29 Mimic as a Therapy for Pulmonary Fibrosis

microRNAs are non-coding RNAs that negatively regulate gene networks. Previously, we reported a systemically delivered miR-29 mimic MRG-201 that reduced fibrosis in animal models, but at doses prohibiting clinical translation. Here, we generated MRG-229, a next-gen miR-29 mimic with improved chemical stability, conjugated with the internalization moiety BiPPB (PDGFbetaR-specific bicyclic peptide). In TGF-b-treated human lung fibroblasts and precision cut lung slices, MRG-229 decreased COL1A1 and ACTA2 gene expression and reduced collagen production. In bleomycin-treated mice, intravenous or subcutaneous delivery of MRG-229 downregulated profibrotic gene programs at doses more than ten-fold lower than the original compound. In rats and non-human primates, and at clinically relevant doses, MRG-229 was well tolerated, with no adverse findings observed. In human peripheral blood decreased mir-29 concentrations were associated with increased mortality in two cohorts potentially identified as a target population for treatment. Collectively, our results provide support for the development of MRG-229 as a potential therapy in humans with IPF. One Sentence SummaryOne Sentence Summary: A stabilized, next-generation miR-29 mimic has been developed that demonstrates efficacy at commercially viable doses with a robust safety margin in non-human primates.

molecular biology↗

bioRxiv2021

Immunogenic Potential of DNA Vaccine candidate, ZyCoV-D against SARS-CoV-2 in Animal Models

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), initially originated in China in year 2019 and spread rapidly across the globe within 5 months, causing over 96 million cases of infection and over 2 million deaths. Huge efforts were undertaken to bring the COVID-19 vaccines in clinical development, so that it can be made available at the earliest, if found to be efficacious in the trials. We developed a candidate vaccine ZyCoV-D comprising of a DNA plasmid vector carrying the gene encoding the spike protein (S) of the SARS-CoV-2 virus. The S protein of the virus includes the receptor binding domain (RBD), responsible for binding to the human angiotensin converting enzyme (ACE-2) receptor. The DNA plasmid construct was transformed into E. coli cells for large scale production. The immunogenicity potential of the plasmid DNA has been evaluated in mice, guinea pig, and rabbit models by intradermal route at 25, 100 and 500g dose. Based on the animal studies proof-of-concept has been established and preclinical toxicology (PCT) studies were conducted in rat and rabbit model. Preliminary animal study demonstrates that the candidate DNA vaccine induces antibody response including neutralizing antibodies against SARS-CoV-2 and also provided Th-1 response as evidenced by elevated IFN-{gamma} levels.

immunology↗

bioRxiv2019

Genome-wide association study of susceptibility to idiopathic pulmonary fibrosis

RationaleIdiopathic pulmonary fibrosis (IPF) is a complex lung disease characterised by scarring of the lung that is believed to result from an atypical response to injury of the epithelium. The mechanisms by which this arises are poorly understood and it is likely that multiple pathways are involved. The strongest genetic association with IPF is a variant in the promoter of MUC5B where each copy of the risk allele confers a five-fold risk of disease. However, genome-wide association studies have reported additional signals of association implicating multiple pathways including host defence, telomere maintenance, signalling and cell-cell adhesion.\n\nObjectivesTo improve our understanding of mechanisms that increase IPF susceptibility by identifying previously unreported genetic associations.\n\nMethods and measurementsWe performed the largest genome-wide association study undertaken for IPF susceptibility with a discovery stage comprising up to 2,668 IPF cases and 8,591 controls with replication in an additional 1,467 IPF cases and 11,874 controls. Polygenic risk scores were used to assess the collective effect of variants not reported as associated with IPF.\n\nMain resultsWe identified and replicated three new genome-wide significant (P<5x10-8) signals of association with IPF susceptibility (near KIF15, MAD1L1 and DEPTOR) and confirm associations at 11 previously reported loci. Polygenic risk score analyses showed that the combined effect of many thousands of as-yet unreported IPF risk variants contribute to IPF susceptibility.\n\nConclusionsNovel association signals support the importance of mTOR signalling in lung fibrosis and suggest a possible role of mitotic spindle-assembly genes in IPF susceptibility.

genomics↗

bioRxiv2018

Patient reported distress can aid clinical decision making in idiopathic pulmonary fibrosis: analysis of the PROFILE cohort

Idiopathic pulmonary fibrosis is a progressive and fatal interstitial lung disease. We aimed to determine if patient response to a palliative assessment survey could predict disease progression or death. We undertook a cross-sectional study in a UK clinical cohort of incident cases. Rasch-based methodology provided a disease distress value from an abridged 11 item model of the original 45 item survey. Distress values were compared with measures of lung function. Disease progression or mortality alone was predicted at twelve months from survey completion, with risk of death assessed at three, six and twelve months. Disease distress values were negatively correlated with lung function (r=-0.275 percent predicted DLCO). Expected survey scores computed from distress values could distinguish disease progression, 8.8 (p=0.004), and people who died, 10.2 (p=0.002), from those who did not progress, 6.9. Actual survey scores predicted disease progression and mortality with an area under the curve of 0.60 and 0.64, respectively. Each point increment in actual score increased risk of twelve-month mortality by 10%, almost 43% of people scoring above 18 did not survive beyond 105 days. We define a short questionnaire that can score disease distress and predict prognosis, assisting clinical decision making in progressive fibrosis.

clinical trials↗