Wing-Cheong Wong

Gene expression profiling reveals the defining features of the classical, intermediate, and nonclassical human monocyte subsets.

New official nomenclature subdivides human monocytes into 3 subsets: the classical (CD14(++)CD16(-)), intermediate (CD14(++)CD16(+)), and nonclassical (CD14(+)CD16(++)) monocytes. This introduces new challenges, as monocyte heterogeneity is mostly understood based on 2 subsets, the CD16(-) and CD16(+) monocytes. Here, we comprehensively defined the 3 circulating human monocyte subsets using microarray, flow cytometry, and cytokine production analysis. We find that intermediate monocytes expressed a large majority (87%) of genes and surface proteins at levels between classical and nonclassical monocytes. This establishes their intermediary nature at the molecular level. We unveil the close relationship between the intermediate and nonclassic monocytes, along with features that separate them. Intermediate monocytes expressed highest levels of major histocompatibility complex class II, GFRα2 and CLEC10A, whereas nonclassic monocytes were distinguished by cytoskeleton rearrangement genes, inflammatory cytokine production, and CD294 and Siglec10 surface expression. In addition, we identify new features for classic monocytes, including AP-1 transcription factor genes, CLEC4D and IL-13Rα1 surface expression. We also find circumstantial evidence supporting the developmental relationship between the 3 subsets, including gradual changes in maturation genes and surface markers. By comprehensively defining the 3 monocyte subsets during healthy conditions, we facilitate target identification and detailed analyses of aberrations that may occur to monocyte subsets during diseases.

Mesenchymal Transition and Dissemination of Cancer Cells Is Driven by Myeloid-Derived Suppressor Cells Infiltrating the Primary Tumor

In order to metastasize, cancer cells need to acquire a motile phenotype. Previously, development of this phenotype was thought to rely on the acquisition of selected, random mutations and thus would occur late in cancer progression. However, recent studies show that cancer cells disseminate early, implying the existence of a different, faster route to the metastatic motile phenotype. Using a spontaneous murine model of melanoma, we show that a subset of bone marrow-derived immune cells (myeloid-derived suppressor cells or MDSC) preferentially infiltrates the primary tumor and actively promotes cancer cell dissemination by inducing epithelial-mesenchymal transition (EMT). CXCL5 is the main chemokine attracting MDSC to the primary tumor. In vitro assay using purified MDSC showed that TGF-β, EGF, and HGF signaling pathways are all used by MDSC to induce EMT in cancer cells. These findings explain how cancer cells acquire a motile phenotype so early and provide a mechanistic explanation for the long recognized link between inflammation and cancer progression.

Identification of Novel Functional Differences in Monocyte Subsets Using Proteomic and Transcriptomic Methods

Human blood monocytes can be broadly divided into two distinct subsets: CD14+CD16- and CD14+/lowCD16+ subsets. Perturbation in their proportions in the blood has been observed in several disease conditions. Although numerous phenotypic and functional differences between the two subsets have already been described, the roles contributed by each subset during homeostasis or disease conditions are still largely unclear. To uncover novel differences to aid in elucidating their functions, we perform a global analysis of the two subsets utilizing both proteomics and transcriptomics approaches. From the proteomics and transcriptomics data, the expression of 613 genes by the two subsets is detected at both the protein and mRNA levels. These 613 genes are assessed for up-regulation in each subset at the protein and mRNA levels using a cutoff fold change of > or =|1.5| between subsets. Proteins and mRNAs up-regulated in each subset are then mapped in silico into biological functions. This mapping reveals copious functional differences between the subsets, many of which are seen at both protein and mRNA levels. For instance, expression of genes involved in F(CY) receptor-mediated phagocytosis are up-regulated in the CD14+/lowCD16+ subset, while those involved in antimicrobial function are up-regulated in the CD14+CD16- subset. We uncover novel functional differences between the monocyte subsets from differences in gene expression at the protein and mRNA levels. These functional differences would provide new insights into the different roles of the two monocyte subsets in regulating innate and adaptive immune responses.

Macrophages in human colorectal cancer are pro-inflammatory and prime T cells towards an anti-tumour type-1 inflammatory response.

High macrophage infiltration into tumours often correlates with poor prognoses; in colorectal, stomach and skin cancers, however, the opposite is observed but the mechanisms behind this phenomenon remain unclear. Here, we sought to understand how tumour‐associated macrophages (TAMs) in colorectal cancer execute tumour‐suppressive roles. We found that TAMs in a colorectal cancer model were pro‐inflammatory and inhibited the proliferation of tumour cells. TAMs also produced chemokines that attract T cells, stimulated proliferation of allogeneic T cells and activated type‐1 T cells associated with anti‐tumour immune responses. Using colorectal tumour tissues, we verified that TAMs in vivo were indeed pro‐inflammatory. Furthermore, the number of tumour‐infiltrating T cells correlated with the number of TAMs, suggesting that TAMs could attract T cells; and indeed, type‐1 T cells were present in the tumour tissues. Patient clinical data suggested that TAMs exerted tumour‐suppressive effects with the help of T cells. Hence, the tumour‐suppressive mechanisms of TAMs in colorectal cancer involve the inhibition of tumour cell proliferation alongside the production of pro‐inflammatory cytokines, chemokines and promoting type‐1 T‐cell responses. These new findings would contribute to the development of future cancer immunotherapies based on enhancing the tumour‐suppressive properties of TAMs to boost anti‐tumour immune responses.

Genus-Wide Comparative Genomics of Malassezia Delineates Its Phylogeny, Physiology, and Niche Adaptation on Human Skin

Malassezia is a unique lipophilic genus in class Malasseziomycetes in Ustilaginomycotina, (Basidiomycota, fungi) that otherwise consists almost exclusively of plant pathogens. Malassezia are typically isolated from warm-blooded animals, are dominant members of the human skin mycobiome and are associated with common skin disorders. To characterize the genetic basis of the unique phenotypes of Malassezia spp., we sequenced the genomes of all 14 accepted species and used comparative genomics against a broad panel of fungal genomes to comprehensively identify distinct features that define the Malassezia gene repertoire: gene gain and loss; selection signatures; and lineage-specific gene family expansions. Our analysis revealed key gene gain events (64) with a single gene conserved across all Malassezia but absent in all other sequenced Basidiomycota. These likely horizontally transferred genes provide intriguing gain-of-function events and prime candidates to explain the emergence of Malassezia. A larger set of genes (741) were lost, with enrichment for glycosyl hydrolases and carbohydrate metabolism, concordant with adaptation to skin’s carbohydrate-deficient environment. Gene family analysis revealed extensive turnover and underlined the importance of secretory lipases, phospholipases, aspartyl proteases, and other peptidases. Combining genomic analysis with a re-evaluation of culture characteristics, we establish the likely lipid-dependence of all Malassezia. Our phylogenetic analysis sheds new light on the relationship between Malassezia and other members of Ustilaginomycotina, as well as phylogenetic lineages within the genus. Overall, our study provides a unique genomic resource for understanding Malassezia niche-specificity and potential virulence, as well as their abundance and distribution in the environment and on human skin.

More Than 1,001 Problems with Protein Domain Databases: Transmembrane Regions, Signal Peptides and the Issue of Sequence Homology

Large-scale genome sequencing gained general importance for life science because functional annotation of otherwise experimentally uncharacterized sequences is made possible by the theory of biomolecular sequence homology. Historically, the paradigm of similarity of protein sequences implying common structure, function and ancestry was generalized based on studies of globular domains. Having the same fold imposes strict conditions over the packing in the hydrophobic core requiring similarity of hydrophobic patterns. The implications of sequence similarity among non-globular protein segments have not been studied to the same extent; nevertheless, homology considerations are silently extended for them. This appears especially detrimental in the case of transmembrane helices (TMs) and signal peptides (SPs) where sequence similarity is necessarily a consequence of physical requirements rather than common ancestry. Thus, matching of SPs/TMs creates the illusion of matching hydrophobic cores. Therefore, inclusion of SPs/TMs into domain models can give rise to wrong annotations. More than 1001 domains among the 10,340 models of Pfam release 23 and 18 domains of SMART version 6 (out of 809) contain SP/TM regions. As expected, fragment-mode HMM searches generate promiscuous hits limited to solely the SP/TM part among clearly unrelated proteins. More worryingly, we show explicit examples that the scores of clearly false-positive hits, even in global-mode searches, can be elevated into the significance range just by matching the hydrophobic runs. In the PIR iProClass database v3.74 using conservative criteria, we find that at least between 2.1% and 13.6% of its annotated Pfam hits appear unjustified for a set of validated domain models. Thus, false-positive domain hits enforced by SP/TM regions can lead to dramatic annotation errors where the hit has nothing in common with the problematic domain model except the SP/TM region itself. We suggest a workflow of flagging problematic hits arising from SP/TM-containing models for critical reconsideration by annotation users.

Dacarbazine promotes stromal remodeling and lymphocyte infiltration in cutaneous melanoma lesions.

Dacarbazine (DTIC) is the standard first-line drug for advanced stage melanoma, but it induces objective clinical responses in only 15% of patients. This study was designed to identify molecular changes specifically induced by treatment in chemo-sensitive lesions. Using global transcriptome analysis and immunohistochemistry, we analyzed cutaneous metastases resected from patients with melanoma before and after DTIC treatment. The treatment induced similar functional changes in different lesions from the same patient. Stromal and immune response-related genes were the most frequently upregulated, particularly in lesions that responded to treatment by stabilizing or regressing. T-cell infiltration and enhanced major histocompatibility complex class II expression were observed in a subset of patients. Stable, chemo-sensitive lesions exhibited activation of genetic programs related to extracellular matrix remodeling, including increased expression of secreted protein acidic and rich in cysteine (SPARC) by tumor cells. These events were associated with local response to treatment and with superior survival in our group of patients. In contrast, SPARC expression was downregulated in lesions resistant to DTIC. Thus, chemotherapy drugs originally selected for their direct cytotoxicity to tumor cells may also influence disease progression by inducing changes in the tumor microenvironment.

Not all transmembrane helices are born equal: Towards the extension of the sequence homology concept to membrane proteins

BackgroundSequence homology considerations widely used to transfer functional annotation to uncharacterized protein sequences require special precautions in the case of non-globular sequence segments including membrane-spanning stretches composed of non-polar residues. Simple, quantitative criteria are desirable for identifying transmembrane helices (TMs) that must be included into or should be excluded from start sequence segments in similarity searches aimed at finding distant homologues.ResultsWe found that there are two types of TMs in membrane-associated proteins. On the one hand, there are so-called simple TMs with elevated hydrophobicity, low sequence complexity and extraordinary enrichment in long aliphatic residues. They merely serve as membrane-anchoring device. In contrast, so-called complex TMs have lower hydrophobicity, higher sequence complexity and some functional residues. These TMs have additional roles besides membrane anchoring such as intra-membrane complex formation, ligand binding or a catalytic role. Simple and complex TMs can occur both in single- and multi-membrane-spanning proteins essentially in any type of topology. Whereas simple TMs have the potential to confuse searches for sequence homologues and to generate unrelated hits with seemingly convincing statistical significance, complex TMs contain essential evolutionary information.ConclusionFor extending the homology concept onto membrane proteins, we provide a necessary quantitative criterion to distinguish simple TMs (and a sufficient criterion for complex TMs) in query sequences prior to their usage in homology searches based on assessment of hydrophobicity and sequence complexity of the TM sequence segments.ReviewersThis article was reviewed by Shamil Sunyaev, L. Aravind and Arcady Mushegian.

Reconstruction of gene association network reveals a transmembrane protein required for adipogenesis and targeted by PPARγ.

We have developed a method for reconstructing gene association networks and have applied this method to gene profiles from 3T3-L1 cells. Priorization of the candidate genes pinpointed a transcript annotated as APMAP (adipocyte plasma membrane-associated protein). Functional studies showed that APMAP is upregulated in murine and human adipogenic cell models as well as in a genetic mouse model of obesity. Silencing APMAP in 3T3-L1 cells strongly impaired the differentiation into adipocytes. Moreover, APMAP expression was strongly induced by the PPARγ ligand rosiglitazone in adipocytes in vitro and in vivo in adipose tissue. Using ChIP-qPCR and luciferase reporter assays, we show a functional PPARγ binding site. In addition, we provide evidence that the extracellular C-terminal domain of APMAP is required for the function of APMAP in adipocyte differentiation. Finally, we demonstrate that APMAP translocates from the endoplasmatic reticulum to the plasma membrane during adipocyte differentiation.

R-POPTVR: A Novel Reinforcement-Based POPTVR Fuzzy Neural Network for Pattern Classification

In general, a fuzzy neural network (FNN) is characterized by its learning algorithm and its linguistic knowledge representation. However, it does not necessarily interact with its environment when the training data is assumed to be an accurate description of the environment under consideration. In interactive problems, it would be more appropriate for an agent to learn from its own experience through interactions with the environment, i.e., reinforcement learning. In this paper, three clustering algorithms are developed based on the reinforcement learning paradigm. This allows a more accurate description of the clusters as the clustering process is influenced by the reinforcement signal. They are the REINFORCE clustering technique I (RCT-I), the REINFORCE clustering technique II (RCT-II), and the episodic REINFORCE clustering technique (ERCT). The integrations of the RCT-I, the RCT-II, and the ERCT within the pseudo-outer product truth value restriction (POPTVR), which is a fuzzy neural network integrated with the truth restriction value (TVR) inference scheme in its five layered feedforward neural network, form the RPOPTVR-I, the RPOPTVR-II, and the ERPOPTVR, respectively. The Iris, Phoneme, and Spiral data sets are used for benchmarking. For both Iris and Phoneme data, the RPOPTVR is able to yield better classification results which are higher than the original POPTVR and the modified POPTVR over the three test trials. For the Spiral data set, the RPOPTVR-II is able to outperform the others by at least a margin of 5.8% over multiple test trials. The three reinforcement-based clustering techniques applied to the POPTVR network are able to exhibit the trial-and-error search characteristic that yields higher qualitative performance.