Biology subjects

Saha, A.

Publications and source records attributed to Saha, A..

95 records · Page 6Linked to original sources

bioRxiv2018

Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available GTEx tissues and find 2356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

genetics↗

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

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.\n\nTo identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.\n\nWe observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis-eQTLs to pinpoint causal genes within susceptibility loci.\n\nIn contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.\n\nWe observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

genomics↗

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↗

bioRxiv2018

Non-histone human chromatin protein, PC4 is critical for genomic integrity and negatively regulates autophagy

Multifunctional human transcriptional positive co-activator 4 (PC4) is a bonafide non-histone component of the chromatin and plays a pivotal role in the process of chromatin compaction and functional genome organization. Knockdown of PC4 expression leads to a drastic open conformation of the chromatin and thereby altered nuclear architecture, defects in chromosome segregation and changed epigenetic landscape. Interestingly, these defects do not induce cellular death but result in enhanced cellular proliferation possibly through enhanced autophagic activity. PC4 depletion confers significant resistance to gamma irradiation. Exposure to gamma irradiation further induced autophagy in these cells. Inhibition of autophagy by small molecule inhibitors as well as by silencing of a critical autophagy gene drastically reduces the survivability PC4 knockdown cells. On the contrary, complementation with wild type PC4 could reverse this phenomenon, confirming the process of autophagy as the key mechanism for radiation resistance in absence of PC4. These data connect the unexplored role of chromatin architecture in regulating autophagy during a stress condition such as radiation.

molecular biology↗