Biology subjects

Zhang, M. Q.

Publications and source records attributed to Zhang, M. Q..

5 recordsLinked to original sources

bioRxiv2018

Integrating Hi-C and FISH data for modeling 3D organizations of chromosomes

The new advances in various experimental techniques that provide complementary in-formation about the spatial conformations of chromosomes have inspired researchers to develop computational methods to fully exploit the merits of individual data sources and combine them to improve the modeling of chromosome structure. In this paper, we propose GEM-FISH, a first method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model. Comprehensive tests on a set of chromosomes for which both Hi-C and FISH data were available have demonstrated that GEM-FISH can reconstruct the 3D models of chromosomes with more accurate spatial organizations of TADs and compartments than using only Hi-C data. In addition, GEM-FISH can accurately capture the spatial proximity of loop loci and the colocalization of loci from the same sub-compartments. Moreover, our reconstructed 3D models of chromosomes revealed novel patterns of spatial distributions of super-enhancers which can provide useful insights into understanding the functional roles of these super-enhancers in gene regulation. All these results demonstrated that, through integrating both Hi-C and FISH data into a unified framework, GEM-FISH can provide a better tool for modeling the 3D organizations of chromosomes than using the Hi-C data alone.

bioinformatics↗

bioRxiv2018

Germline genetics encode the resistance, risk, and lymphatic metastasis of triple-negative breast cancer in the southern Chinese population

Early identification of the risk for triple-negative breast cancer (TNBC) at the asymptomatic phase could lead to better prognosis. Here we developed a machine learning method to quantify systematic impact of all rare germline mutations on each pathway. We collected 106 TNBC patients and 287 elder healthy women controls. The spectra of activity profiles in multiple pathways were mapped and most pathway activities exhibited globally suppressed by the portfolio of individual germline mutations in TNBC patients. Accordingly, all individuals were delineated into two types: A and B. Type A patients could be differentiated from controls (AUC = 0.89) and sensitive to BRCA1/2 damages; Type B patients can be also differentiated from controls (AUC = 0.69) but probably being protected from BRCA1/2 damages. Further we found that Individuals with the lowest activity of selected pathways had extreme high relative risk (up to 21.67 in type A) and increased lymph node metastasis in these patients. Our study showed that genomic DNA contains information of unimaginable pathogenic factors. And this information is in a distributed form that could be applied to risk assessment for more cancer types. SignificanceWe identified individuals who are more susceptible to triple negative breast cancer. Our method performs much better than previous assessments based on BRCA1/2 damages, even polygenic risk scores. We disclosed previously unimaginable pathogens in a distributed form on genome and extended risk prediction to scenarios for other cancers.

cancer biology↗

bioRxiv2018

Regulation by competition: a hidden layer of gene regulatory network

Molecular competition is ubiquitous, essential and multifunctional throughout diverse biological processes. Competition brings about trade-offs of shared limited resources among the cellular components, and it thus introduce a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. By abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically compare experimental evidences in these processes and analyzed general properties shared behind them. We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets. This work uncovered the complexity and generality of molecular competition effect, which might act as a hidden regulatory mechanism with multiple functions throughout biological networks in both natural and synthetic systems.

systems biology↗

bioRxiv2017

Evolutionary outcomes of plasmid-CRISPR conflicts in an opportunistic pathogen

Enterococcus faecalis is a Gram-positive bacterium that natively colonizes the human gastrointestinal tract and opportunistically causes life-threatening infections. Multidrug-resistant (MDR) E. faecalis strains have emerged that are replete with mobile genetic elements (MGEs). Non-MDR E. faecalis strains frequently possess CRISPR-Cas systems, which reduce the frequency of mobile genetic element (MGE) acquisition. We demonstrated in previous studies that E. faecalis populations can transiently maintain both a functional CRISPR-Cas system and a CRISPR-Cas target. In this study, we used serial passage and deep sequencing to analyze these populations. In the presence of antibiotic selection for the plasmid, mutants with compromised CRISPR-Cas defense and enhanced ability to acquire a second antibiotic resistance plasmid emerged. Conversely, in the absence of selection, the plasmid was lost from wild-type E. faecalis populations, but not E. faecalis populations that lacked the cas9 gene. Our results indicate that E. faecalis CRISPR-Cas can become compromised under antibiotic selection, generating populations with enhanced abilities to undergo horizontal gene transfer. ImportanceEnterococcus faecalis is a leading cause of hospital-acquired infections and disseminator of antibiotic resistance plasmids among Gram-positive bacteria. We have previously shown that E. faecalis strains with an active CRISPR-Cas system can prevent plasmid acquisition and thus limit the transmission of antibiotic resistance determinants. Yet, CRISPR-Cas was not a perfect barrier. In this study, we observed populations of E. faecalis with transient co-existence of CRISPR-Cas and one of its plasmid targets. Our experimental data demonstrate that antibiotic selection results in compromised E. faecalis CRISPR-Cas function, thereby facilitating the acquisition of additional resistance plasmids by E. faecalis.

microbiology↗

bioRxiv2017

Comparative transcriptome profiling of the human and mouse dorsal root ganglia: An RNA-seq-based resource for pain and sensory neuroscience research

Molecular neurobiological insight into human nervous tissues is needed to generate next generation therapeutics for neurological disorders like chronic pain. We obtained human Dorsal Root Ganglia (DRG) samples from organ donors and performed RNA-sequencing (RNA-seq) to study the human DRG (hDRG) transcriptional landscape, systematically comparing it with publicly available data from a variety of human and orthologous mouse tissues, including mouse DRG (mDRG). We characterized the hDRG transcriptional profile in terms of tissue-restricted gene co-expression patterns and putative transcriptional regulators, and formulated an information-theoretic framework to quantify DRG enrichment. Our analyses reveal an hDRG-enriched protein-coding gene set (~140), some of which have not been described in the context of DRG or pain signaling. A majority of these show conserved enrichment in mDRG, and were mined for known drug - gene product interactions. Comparison of hDRG and tibial nerve transcriptomes suggest pervasive mRNA transport of sensory neuronal genes to axons in adult hDRG, with potential implications for mechanistic insight into chronic pain in patients. Relevant gene families and pathways were also analyzed, including transcription factors (TFs), g-protein coupled receptors (GCPRs) and ion channels. We present our work as an online, searchable repository (http://www.utdallas.edu/bbs/painneurosciencelab/DRGtranscriptome), creating a valuable resource for the community. Our analyses provide insight into DRG biology for guiding development of novel therapeutics, and a blueprint for cross-species transcriptomic analyses.\n\nSummaryWe generated RNA sequencing data from human DRG samples and comprehensively compared this transcriptome to other human tissues and a matching panel of mouse tissues. Our analysis uncovered functionally enriched genes in the human and mouse DRG with important implications for understanding sensory biology and pain drug discovery.

neuroscience↗