Monozygotic twins discordant for schizophrenia differ in maturation and synaptic transmission.

Schizophrenia affects approximately 1% of the world population. Genetics, epigenetics, and environmental factors are known to play a role in this psychiatric disorder. While there is a high concordance in monozygotic twins, about half of twin pairs are discordant for schizophrenia. To address the question of how and when concordance in monozygotic twins occur, we have obtained fibroblasts from two pairs of schizophrenia discordant twins (one sibling with schizophrenia while the second one is unaffected by schizophrenia) and three pairs of healthy twins (both of the siblings are healthy). We have prepared iPSC models for these 3 groups of patients with schizophrenia, unaffected co-twins, and the healthy twins. When the study started the co-twins were considered healthy and unaffected but both the co-twins were later diagnosed with a depressive disorder. The reprogrammed iPSCs were differentiated into hippocampal neurons to measure the neurophysiological abnormalities in the patients. We found that the neurons derived from the schizophrenia patients were less arborized, were hypoexcitable with immature spike features, and exhibited a significant reduction in synaptic activity with dysregulation in synapse-related genes. Interestingly, the neurons derived from the co-twin siblings who did not have schizophrenia formed another distinct group that was different from the neurons in the group of the affected twin siblings but also different from the neurons in the group of the control twins. Importantly, their synaptic activity was not affected. Our measurements that were obtained from schizophrenia patients and their monozygotic twin and compared also to control healthy twins point to hippocampal synaptic deficits as a central mechanism in schizophrenia.

miR156-PvSPL2 controls culm development by transcriptional repression of switchgrass CYTOKININ OXIDASE/DEHYDROGENASE4.

Culm development in grasses can be controlled by both miR156 and cytokinin. However, the crosstalk between the miR156-SPL module and the cytokinin metabolic pathway remains largely unknown. Here, we found CYTOKININ OXIDASE/DEHYDROGENASE4 (PvCKX4) plays a negative regulatory role in culm development of the bioenergy grass Panicum virgatum (switchgrass). Overexpression of PvCKX4 in switchgrass reduced the internode diameter and length without affecting tiller number. Interestingly, we also found that PvCKX4 was always upregulated in miR156 overexpressing (miR156OE) transgenic switchgrass lines. Additionally, upregulation of either miR156 or PvCKX4 in switchgrass reduced the content of isopentenyl adenine (iP) without affecting trans-zeatin (tZ) accumulation. It is consistent with the evidence that the recombinant PvCKX4 protein exhibited much higher catalytic activity against iP than tZ in vitro. Furthermore, our results showed that miR156-targeted SPL2 bound directly to the promoter of PvCKX4 to repress its expression. Thus, alleviating the SPL2-mediated transcriptional repression of PvCKX4 through miR156 overexpression resulted in a significant increase in cytokinin degradation and impaired culm development in switchgrass. On the contrary, suppressing PvCKX4 in miR156OE transgenic plants restored iP content, internode diameter, and length to wild-type levels. Most strikingly, the double transgenic lines retained the same increased tiller numbers as the miR156OE transgenic line, which yielded more biomass than the wild type. These findings indicate that the miR156-SPL module can control culm development through transcriptional repression of PvCKX4 in switchgrass, which provides a promising target for precise design of shoot architecture to yield more biomass from grasses.

Multiomics atlas-assisted discovery of transcription factors enables specific cell state programming.

The same types of cells can assume diverse states with varying functionalities. Effective cell therapy can be achieved by specifically driving a desirable cell state, which requires the elucidation of key transcription factors (TFs). Here, we integrated epigenomic and transcriptomic data at the systems level to identify TFs that define different CD8 + T cell states in an unbiased manner. These TF profiles can be used for cell state programming that aims to maximize the therapeutic potential of T cells. For example, T cells can be programmed to avoid a terminal exhaustion state (Tex Term ), a dysfunctional T cell state that is often found in tumors or chronic infections. However, Tex Term exhibits high similarity with the beneficial tissue-resident memory T states (T RM ) in terms of their locations and transcription profiles. Our bioinformatic analysis predicted Zscan20 , a novel TF, to be uniquely active in Tex Term . Consistently, Zscan20 knock-out thwarted the differentiation of Tex Term in vivo , but not that of T RM . Furthermore, perturbation of Zscan20 programs T cells into an effector-like state that confers superior tumor and virus control and synergizes with immune checkpoint therapy. We also identified Jdp2 and Nfil3 as powerful Tex Term drivers. In short, our multiomics-based approach discovered novel TFs that enhance anti-tumor immunity, and enable highly effective cell state programming. One sentence summary: Multiomics atlas enables the systematic identification of cell-state specifying transcription factors for therapeutic cell state programming.

Discovery of eQTL Alleles Associated with Autism Spectrum Disorder: A Case-Control Study.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by challenges in social communication as well as repetitive or restrictive behaviors. Many genetic associations with ASD have been identified, but most associations occur in a fraction of the ASD population. Here, we searched for eQTL-associated DNA variants with significantly different allele distributions between ASD-affected and control. Thirty significant DNA variants associated with 174 tissue-specific eQTLs from ASD individuals in the SPARK project were identified. Several significant variants fell within brain-specific regulatory regions or had been associated with a significant change in gene expression in the brain. These eQTLs are a new class of biomarkers that could control the myriad of brain and non-brain phenotypic traits seen in ASD-affected individuals.

Identification of condition-specific regulatory mechanisms in normal and cancerous human lung tissue.

BACKGROUND: Lung cancer is the leading cause of cancer death in both men and women. The most common lung cancer subtype is non-small cell lung carcinoma (NSCLC) comprising about 85% of all cases. NSCLC can be further divided into three subtypes: adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and large cell lung carcinoma. Specific genetic mutations and epigenetic aberrations play an important role in the developmental transition to a specific tumor subtype. The elucidation of normal lung versus lung tumor gene expression patterns and regulatory targets yields biomarker systems that discriminate lung phenotypes (i.e., biomarkers) and provide a foundation for the discovery of normal and aberrant gene regulatory mechanisms. RESULTS: We built condition-specific gene co-expression networks (csGCNs) for normal lung, LUAD, and LUSC conditions. Then, we integrated normal lung tissue-specific gene regulatory networks (tsGRNs) to elucidate control-target biomarker systems for normal and cancerous lung tissue. We characterized co-expressed gene edges, possibly under common regulatory control, for relevance in lung cancer. CONCLUSIONS: Our approach demonstrates the ability to elucidate csGCN:tsGRN merged biomarker systems based on gene expression correlation and regulation. The biomarker systems we describe can be used to classify and further describe lung specimens. Our approach is generalizable and can be used to discover and interpret complex gene expression patterns for any condition or species.

In vivo partial cellular reprogramming enhances liver plasticity and regeneration.

Mammals have limited regenerative capacity, whereas some vertebrates, like fish and salamanders, are able to regenerate their organs efficiently. The regeneration in these species depends on cell dedifferentiation followed by proliferation. We generate a mouse model that enables the inducible expression of the four Yamanaka factors (Oct-3/4, Sox2, Klf4, and c-Myc, or 4F) specifically in hepatocytes. Transient in vivo 4F expression induces partial reprogramming of adult hepatocytes to a progenitor state and concomitantly increases cell proliferation. This is indicated by reduced expression of differentiated hepatic-lineage markers, an increase in markers of proliferation and chromatin modifiers, global changes in DNA accessibility, and an acquisition of liver stem and progenitor cell markers. Functionally, short-term expression of 4F enhances liver regenerative capacity through topoisomerase2-mediated partial reprogramming. Our results reveal that liver-specific 4F expression in vivo induces cellular plasticity and counteracts liver failure, suggesting that partial reprogramming may represent an avenue for enhancing tissue regeneration.

Identification of condition-specific biomarker systems in uterine cancer.

Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene coexpression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential coregulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared with previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.

Exploration into biomarker potential of region-specific brain gene co-expression networks.

The human brain is a complex organ that consists of several regions each with a unique gene expression pattern. Our intent in this study was to construct a gene co-expression network (GCN) for the normal brain using RNA expression profiles from the Genotype-Tissue Expression (GTEx) project. The brain GCN contains gene correlation relationships that are broadly present in the brain or specific to thirteen brain regions, which we later combined into six overarching brain mini-GCNs based on the brain's structure. Using the expression profiles of brain region-specific GCN edges, we determined how well the brain region samples could be discriminated from each other, visually with t-SNE plots or quantitatively with the Gene Oracle deep learning classifier. Next, we tested these gene sets on their relevance to human tumors of brain and non-brain origin. Interestingly, we found that genes in the six brain mini-GCNs showed markedly higher mutation rates in tumors relative to matched sets of random genes. Further, we found that cortex genes subdivided Head and Neck Squamous Cell Carcinoma (HNSC) tumors and Pheochromocytoma and Paraganglioma (PCPG) tumors into distinct groups. The brain GCN and mini-GCNs are useful resources for the classification of brain regions and identification of biomarker genes for brain related phenotypes.

NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles.

Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.

Switchgrass SBP-box transcription factors PvSPL1 and 2 function redundantly to initiate side tillers and affect biomass yield of energy crop.

BACKGROUND: Switchgrass (Panicum virgatum L.) is a dedicated lignocellulosic feedstock for bioenergy production. The SQUAMOSA PROMOTER-BINDING PROTEIN (SBP-box)-LIKE transcription factors (SPLs) change plant architecture and vegetative-to-reproductive phase transition significantly, and as such, they are promising candidates for genetic improvement of switchgrass biomass yield. However, the genome-wide identification and functional characterization of SPL genes have yet to be investigated in herbaceous energy crops. RESULTS: We identified 35 full-length SPL genes in the switchgrass genome. The phylogenetic relationship and expression pattern of PvSPLs provided baseline information for their function characterization. Based on the global overview of PvSPLs, we explored the biological function of miR156-targeted PvSPL1 and PvSPL2, which are closely related members of SPL family in switchgrass. Our results showed that PvSPL1 and PvSPL2 acted redundantly to modulate side tiller initiation, whereas they did not affect phase transition and internode initiation. Consistently, overexpression of the miR156-resistant rPvSPL2 in the miR156-overexpressing transgenic plants greatly reduced tiller initiation, but did not rescue the delayed flowering and increased internode numbers. Furthermore, suppression of PvSPL2 activity in switchgrass increased biomass yield and reduced lignin accumulation, which thereby elevated the total amount of solubilized sugars. CONCLUSIONS: Our results indicate that different miR156-targeted PvSPL subfamily genes function predominantly in certain biological processes in switchgrass. We suggest that PvSPL2 and its paralogs can be utilized as the valuable targets in molecular breeding of energy crops for developing novel germplasms with high biofuel production.

[Transcriptome analysis of Dunaliella viridis].

In order to understand the gene information, function, haloduric pathway (glycerolipid metabolism) and related key genes for Dunaliella viridis, we used Illumina HiSeqTM 2000 high-throughput sequencing technology to sequence its transcriptome. Trinity soft was used to assemble the data to form transcripts. Based on the Clusters of Orthologous Groups (COG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG ) databases, we carried out functional annotation and classification, pathway annotation, and the opening reading fragment (ORF) sequence prediction of transcripts. The key genes in the glycerolipid metabolism were analyzed. The results suggested that 81,593 transcripts were found, and 77,117 ORF sequences were predicted, accounting for 94.50% of all transcripts. COG classification results showed that 16,569 transcripts were assigned to 24 categories. GO classification annotated 76,436 transcripts. The number of transcripts for biologcial processes was 30,678, accounting for 40.14% of all transcripts. KEGG pathway analysis showed that 26,428 transcripts were annotated to 317 pathways, and 131 pathways were related to metabolism, accounting for 41.32% of all annotated pathways. Only one transcript was annotated as coding the key enzyme dihydroxyacetone kinase involved in the glycerolipid pathway. This enzyme could be related to glycerol biosynthesis under salt stress. This study further improved the gene information and laid the foundation of metabolic pathway research for Dunaliella viridis.